Alex Nesic is the Co-Founder & CRO of Bingo Technologies. Bingo Tech is building purpose-built electric mobility infrastructure for emerging markets – combining a commercial EV (the Bingo E2), a dual-battery system with rapid-swap capability, a network of licensed local fleet operators, and a Cloud Fleet OS that ties vehicle owners, operators, and drivers into one ecosystem. Initial deployment is in Nairobi, Kenya, with South Africa onboarded and additional markets across East and West Africa, Southeast Asia, and Latin America planned through 2027–2028. Production vehicles are set to ship by Q3 2026.
You’re effectively building a marketplace for last-mile electric mobility through Bingo Tech, some describe it as an “Airbnb for EV fleets.” What did you learn from platform businesses like Airbnb that you’ve applied, and where does the model need to evolve for mobility?
The Airbnb analogy is useful because it captures the core idea: we don’t own the vehicles, the drivers, or the operators – we connect them. A vehicle owner in the U.S. buys a Bingo E2, Bingo’s operations team in Nairobi deploys it, and a professional driver earns a living from it. We provide the standards, the software layer, and the infrastructure that makes that trust possible. Closest analogy is Airbnb for commercial vehicles, or Turo with a managed operator on the ground.
What we took from Airbnb specifically: trust is the product. Without verified hosts, transparent pricing, and reliable support, the marketplace collapses no matter how good the underlying asset is. So we put serious effort into operator vetting, real-time visibility through Cloud Fleet OS, and clean, daily reporting on what an owner earned. That’s why our MAYA AI fleet manager exists – owners need answers about utilization, earnings, and vehicle health on demand, not in a monthly PDF.
Where the model has to evolve: mobility is a heavier, more regulated, more physically constrained business than hospitality. An apartment doesn’t move, doesn’t need to be charged, doesn’t need spare parts shipped to Nairobi. So the platform has to do more than match supply and demand – it has to underwrite the asset, run the energy network, certify the vehicle for each market, and handle FX, insurance, and maintenance coordination. That’s why we built the swap network, the OS, and the vehicle ourselves. A pure marketplace play doesn’t work in mobility; you need to own the parts of the stack that would otherwise break the experience.
You’re tackling vehicles, batteries, swapping, and software in one ecosystem. Why has the industry historically struggled with this fragmentation, and what’s the unlock that makes an integrated model viable now?
Historically, every layer was solved by a different company with a different incentive. A vehicle OEM optimized for consumer features. A battery company optimized for cells. A charging operator optimized for kilowatt-hours sold. A fleet operator optimized for driver throughput. The result is a stack that nobody owns end to end – and the cracks between each layer are exactly where the unit economics break for a ride-hail driver working a 12-hour shift.
The specific failure mode in emerging markets: you can’t hand a Nairobi driver a consumer EV designed for suburban commuting in California, plug it into a charging network that doesn’t exist, and expect the math to work. Range anxiety, charging downtime, and import costs eat the savings before they reach the driver. So the integrated model isn’t a strategic preference – it’s the only architecture that produces a vehicle a professional driver will actually choose.
What’s changed to make it viable now: three things converged. Battery costs and chemistry – LFP for durability, semi-solid NCM for swappable energy density – finally support a commercial duty-cycle vehicle at around $12,000. Cloud and mobile infrastructure in markets like Kenya means a driver, an operator, and an owner half a world away can transact in real time. And the operator ecosystem is mature enough that we can plug into licensed local businesses on Uber and Bolt rather than trying to be the ride-hail platform ourselves. That last point matters – we’re not competing with the existing demand layer, we’re upgrading the vehicle and infrastructure underneath it.
Battery swapping has long promised speed and uptime. What’s changed, technologically or commercially, that makes swapping more compelling today for last-mile operators?
To be clear, in Bingo’s case, this is not an either/or question, but an innovative ‘world-first’ dual battery architecture. A built-in 31KwH battery that delivers 350Kms of range and a swappable battery system that exists to extend that and remove any range anxiety. The E2 can support DC fast charging, AC charging, and battery swapping giving the most operational flexibility. For a commercial driver, time is revenue. A ride-hail driver who loses 2-3 hours to charge an EV loses 5 to 8 fares. Across a year, that’s a meaningful share of household income. So the question isn’t “is swapping technically better than charging?” – it’s “which one keeps the vehicle earning during peak hours?” In a duty cycle where the vehicle runs 10 to 16 hours a day, DC fast charge combined with swapping capability is a game changer.
On the Bingo E2, the swappable pack is a 13 kWh NCM semi-solid module designed for a rapid-swap form factor, paired with a fixed 31 kWh LFP pack that handles base range and lasts up to 800,000 kilometers. The driver doesn’t choose between charging and swapping – they get both, and they swap when shift economics demand it.
Commercially, two things have shifted. First, the grid reality in emerging markets makes dense fast-charging build-outs slow and expensive – swap stations are easier to site, electrically lighter, and can be deployed where the rides actually are. Second, ownership of the battery pack moves off the driver’s and the owner’s balance sheet and onto the network, which is the right place for a depreciating energy asset to live. The battery becomes a service, not a sunk cost. That’s the real unlock – not the 2 minutes, but who carries the battery risk.
You’ve spoken about meaningful cost savings for riders. Where exactly do those savings come from, and how do you ensure they’re sustainable as the platform scales?
Let me ground this in the Nairobi numbers, because the savings are concrete and we don’t want to wave at them. In our target markets, gas costs roughly six times what electricity costs per kilometer. For a typical ride-hail driver, that translates to around $11-$14 a day in energy savings on the E2 versus a comparable gas vehicle – which is what allows the driver to double or triple their take-home income while still affording the vehicle lease. That number isn’t hypothetical; it’s what makes the driver choose us, and what keeps them on the platform.
The savings come from four stacked sources. One: a purpose-built EV at around $12,000, not a consumer EV priced for affluent buyers. Two: the energy cost differential between electricity and imported petrol. Three: a swap network that eliminates downtime, so the same vehicle generates more fares per shift. Four: maintenance economics – fewer moving parts, no oil changes, longer service intervals on a chassis built for commercial duty. None of those depend on subsidies or promotional pricing.
On sustainability as we scale: the dangerous version of this story is one where margins are propped up by VC dollars and collapse the moment growth slows. Our structure is the opposite. Owners take title to a real asset. Operators run real businesses on a 15% management fee. Bingo takes a 5% platform fee on lease revenue. Drivers pay an energy cost that’s a sixth of gas. Everyone in the stack earns from operating economics, not from the float. When utilization is high, every party in the chain wins – and that alignment is what makes the savings durable rather than promotional.
From your time across ventures like Drover AI and earlier micromobility businesses, what lessons, good and bad, are shaping how you build this next generation of EV infrastructure?
The first wave of shared micromobility was a masterclass in what happens when you scale before unit economics close. Companies raced to deploy tens of thousands of scooters in dozens of cities, underwriting losses on every ride in the belief that growth would eventually produce margin. It didn’t – because the vehicles weren’t built for the duty cycle, the energy and maintenance infrastructure was bolted on after the fact, and the regulatory relationship was adversarial rather than collaborative. The lesson is that hardware and infrastructure decisions made early are extremely hard to reverse later.
The good lessons we carried forward: real-time fleet telemetry is non-negotiable, and the operating system around the vehicle is at least as important as the vehicle itself. Driver and rider trust is built on consistency, not novelty. And working with regulators from day one – homologating properly, vetting operators, paying duties – is faster in the medium term than trying to ask for forgiveness later.
The bad lessons, which we’re deliberately not repeating: don’t build a fleet you can’t maintain at the unit level. Don’t pick markets because they’re easy to enter; pick markets because the underlying demand actually pays for the vehicle. And don’t conflate growth with traction – every Bingo E2 we ship has to earn its keep on the road. That’s why we’re launching in Nairobi first, with a vehicle purpose-built for that duty cycle and an operator network that’s already running. Discipline early is what buys you scale later.
AI and computer vision have been central to your previous work. How do you see AI transforming fleet efficiency, safety, and utilisation over the next 3–5 years?
The honest answer is that most of the AI value in fleet operations over the next three to five years is going to come from the unglamorous middle of the stack – predictive maintenance, utilization optimization, fraud and misuse detection, dynamic dispatch – not from autonomy on emerging-market streets. Robotaxis are a U.S. and China story for the next decade. Africa needs the operating layer to get smarter underneath a human driver.
That’s what MAYA is, in our Cloud Fleet OS. An owner can ask, in plain language, which of their vehicles is underperforming, why, and what to do about it – and MAYA pulls from utilization data, battery health, operator metrics, and earnings history to give a real answer. Operators get the same intelligence pointed at fleet-level decisions: which drivers to retain, which routes are starving, which packs are degrading. Drivers get a vehicle that flags issues before they become breakdowns.
Where I think it goes over the next three to five years: AI moves from a dashboard layer to an active coordination layer. Battery allocation across swap stations gets optimized in real time against demand forecasts. Insurance pricing becomes dynamic and per-vehicle based on actual operating signature, not broad actuarial buckets. Safety systems use cabin and road-facing cameras to coach driver behavior in the moment, which compounds into lower accident rates and cheaper premiums. And in markets like Nairobi, where rides per vehicle are high and operating conditions are harsh, the efficiency delta from AI is much larger than in markets where vehicles already run well. We’re building for that gap.
A lot of innovation in battery swapping and fleet electrification is happening outside Europe and North America. Where do you see the fastest adoption, and why?
Adoption follows pain. In Europe and North America, gas vehicles still work well enough for the people who own them, and the public charging network is improving fast enough that swap doesn’t feel necessary. In emerging markets, the pain is severe and immediate: gas costs six times electricity and has recently been exacerbated by the Iran conflict, vehicles are 8 to 15 years old on average, urban air quality is among the worst in the world, and a ride-hail driver’s entire household economics depend on shaving cost out of the daily shift. That’s the environment where new mobility infrastructure gets adopted at speed.
Specifically, the fastest adoption over the next five years will be in three regions: East Africa – Kenya, Uganda, Tanzania, Ethiopia, Rwanda – where ride-hail density is high and gas-vehicle import economics are punishing; West Africa – Nigeria, Ghana, Senegal – where the driver population is enormous and government appetite for electrification is real; and Southeast Asia, where two and three-wheeler swap networks are already operating at scale and the four-wheeler category is the next frontier. Latin America is close behind. There are over 10 million ride-hailing drivers across these markets and effectively zero purpose-built EVs serving them today.
The structural reason we deploy capital from the U.S. into these markets is the asymmetry: the cost of an EV plus swap network is largely fixed globally, but the value created – driver income, displaced emissions, fuel import reduction, local job creation – is dramatically higher in markets that have been served last. U.S. and Europe will get robotaxis. China is electrifying its private fleet. We’re focused on the markets where electrification creates the biggest gain in human and environmental terms, and the unit economics happen to support that focus.
If we fast-forward 10 years, what does success look like for your model? Are we heading toward fully decentralised, platform-driven mobility ecosystems, or something more consolidated?
Ten years out, success for us is measurable in three numbers: how many drivers across emerging markets earn a middle-class living because they’re operating a Bingo vehicle, how many tons of CO2 we’ve displaced by retiring aged-import gas fleets, and how many local industries – manufacturing, swap station operations, EV maintenance, fleet management – exist in cities that didn’t have them before. The vehicle is the unit of impact, but the goal is structural transformation of urban mobility in markets that have been overlooked.
On the decentralized-vs-consolidated question, I think it’s both, and that’s the right answer. The asset layer should be decentralized – vehicles owned by individual operators, family offices, local entrepreneurs, diaspora investors. That’s how capital flows into markets that institutional investors won’t touch and how local ownership of mobility infrastructure actually compounds
The coordination layer – the OS, the standards, the swap network, the operator certification – needs to be consolidated, because that’s where trust, interoperability, and safety live. A fragmented OS layer would recreate exactly the problem we set out to solve.
So the end state I’d argue for is platform-mediated, asset-decentralized mobility. Hundreds of thousands of vehicles, owned by tens of thousands of small operators, running on a common infrastructure layer that any qualified operator and any qualified driver can plug into. Profits stay local. The platform takes a thin margin on coordination. And in the markets that have been waiting longest for the mobility transition, it finally arrives – not as a robotaxi pilot, but as a working car that a working driver can earn a living from. That’s the version of the future we’re building toward.
