When Igor Dobrianskyi looks at a car dealership lot, he sees a wasted opportunity.

“Millions and millions of used cars are sitting on parking lots, depreciating and losing value,” Dobrianskyi said in a recent interview, adding that there are 76,000 dealerships in the United States. “At the same time, there are people who need a car for a few months, but the options are actually very limited and expensive.”

Dobrianskyi’s new startup, MyMonthlyCar, aims to connect both sides of that equation through an online platform that offers flexible month-to-month rentals from local dealerships. MyMonthlyCar was selected for the 2026 Startup Battlefield 200, a cohort of promising early-stage startups that have earned a spot to exhibit at this year’s TechCrunch Disrupt, taking place October 13 to 15 in San Francisco.

MyMonthlyCar, which is registered in Delaware and based in Florida, was co-founded by Dobrianskyi; CPO Kostiantyn Gitko; and CTO Vadym Zotov. All three are from Ukraine, said Dobrianskyi, who moved to the U.S. with his wife and young daughter after the Russia-Ukraine war began.

MyMonthlyCar does what its name suggests, with one twist: The startup only rents used cars on a month-to-month basis; no short-term options here. But it does give dealerships the chance to offer customers a rent-to-own option.

“So we don’t bring them only customers to rent on a monthly basis; we basically bring them the clients who potentially can buy this car as well,” he said.

MyMonthlyCar doesn’t charge dealerships to list cars on its website. Instead, MyMonthlyCar charges dealers 10% of each transaction. It also charges the customer a separate 10% fee.

The idea for MyMonthlyCar stems from Dobrianskyi’s previous experience in the industry. The founder owned a car rental company in Ukraine, but the lack of financing options there limited his ability to scale. In 2016, he launched a peer-to-peer car marketplace called SizeCar, where owners rent their cars to other drivers, much like Turo does today. SizeCar eventually spread to 40 European cities.

The startup has signed on seven dealerships to test the service and is working with an insurance broker to finalize its own insurance program, which will let customers choose among different types of coverage.

“Insurance is the key for this business, and you need to have your own insurance as a platform,” he said, noting that the No. 1 question from dealers was about insurance and liability coverage.

Despite its early-stage status, the founders have bullish projections for the startup. They plan to sign on 100 dealerships with 2,000 monthly rentals and $300,000 in revenue in the company’s first year of operation, which will kick off later this year once the insurance program launches. Over the next five years, the goal is to generate $42 million in revenue, Dobrianskyi said.

The startup has yet to raise venture capital and is currently bootstrapped. But Dobrianskyi said the plan is to raise a seed round, with the funds helping the company hire more developers to build out the platform, including an AI tool to help dealers identify which cars are best to rent out at any given time.

To check out MyMonthlyCar and the other startups that are part of TechCrunch’s Startup Battlefield competition (as well as to network with the folks funding them), join us next month at Disrupt.

OpenAI is again experimenting with how its conversational AI assistant, ChatGPT, can help users as they shop online. On Thursday, the company announced the global launch of two new shopping features, including a way to virtually try on clothing and accessories and a new favoriting function that can help users save products they like for later reference.

The updates arrive at a time when AI assistants are exploring consumer use cases around shopping. OpenAI already had to pivot from one of its earlier ideas in this space, an instant checkout feature that ended up not performing well. More recently, agentic AI startup Instinct began pushing product recommendations to users, but some felt that the proactive recommendations were an overreach, more akin to ads than helpful suggestions.

OpenAI said its new shopping features leverage the newly launched ChatGPT Images 2.5 model, which the company claims produces more natural lighting and richer textures, follows editing instructions more reliably, and reduces image generation latency.

To start, virtual try-on allows ChatGPT users to upload a selfie or a full-body photo in order to visualize how an article of clothing or an accessory might look on them. This option will appear as a new “try on” button in ChatGPT’s shopping results. You can also upload an image of an item, like a web screenshot, and ask ChatGPT to try it on for you.

The other new option, Favorites, lets you save products you discover to a Library in the app so you can come back to them later. (These items will be saved alongside your try-on images, the company notes.)

OpenAI said that ChatGPT can help users shop in other ways, too.

For instance, you could describe a style that you’d like to try, then ask it to shop for the pieces needed to complete the look. Or, you could upload photos of celebrities’ outfits and ask it to find the items they’re wearing that are available for purchase.

The latter sees the assistant moving into areas that Pinterest and Google have dominated in recent years as sources for fashion inspiration and discovery that can convert to sales for online retailers.

Whether ChatGPT will become people’s first choice for this type of activity, however, remains to be seen — especially given that Google launched virtual try-on last year.

Google’s prototype of its orbital compute satellite took off today onboard a SpaceX rocket launched from California — the first time the tech giant has sent one of its advanced chips into space.

Built by Planet Labs, the satellite will prove that a Google Tensor Processing Unit, its competitor to Nvidia’s GPUs, can function in space. That means supplying a kilowatt of continuous power, cooling the chip, and running a series of models through their paces to see if anything goes wrong.

“We’ve done testing on the ground, but you know, there’s no test that’s completely as good as the real thing,” said Travis Beals, the Google executive managing Project Suncatcher, the tech giant’s plan to develop large-scale compute clusters in orbit around the Earth.

Once commissioned, the satellite will fire up its TPU in 15-minute bursts to avoid straining the satellite’s power and thermal management systems. This satellite is based on a standard platform built by Planet Labs, but the two companies are working on a demo expected to take flight next year that will see two satellites more purpose-built for advanced compute. Those future versions will attempt to collaborate via a laser communications link.

Suncatcher isn’t the only space AI payload on this SpaceX rocket, which is launching more than one hundred different payloads, including missions from Satlyt and Cowboy Space Company.

What sets the Google initiative apart from those startups (and indeed from SpaceX itself) is that it’s a long-term project.

The focus of this “long-term moonshot,” as Beals puts it, is on building for the space infrastructure and AI workloads that will exist in the future. The company envisions a network of 81 satellites flying in close formation, processing in parallel.

“The bandwidth and the latency between TPUs really, really matters when you’re trying to run a multi-rack workload…we’re trying to look ahead to not just what workloads exist today, but where they will be in five years,” Beals said. That’s largely because the rockets required to scale up orbital data centers in a cost-effective way don’t yet exist.

On Thursday, Google also released a peer-reviewed version of its white paper on orbital data centers, one of the most rigorous analyses available of how compute gets to orbit. The paper will be published in Joule.

One of the paper’s most notable aspects is how Google thinks about access to space. Although the researchers stress their analysis isn’t an economic feasibility study, it offers an interesting picture of how the company sees rockets becoming cheaper over time.

Like all data center companies, Google is looking to SpaceX to get its spacecraft off the ground. (Google is also a major investor in SpaceX.)

Arguing that Elon Musk’s rocket builders have achieved a price-reducing “learning curve” of about 20% a year since they launched the Falcon 1 rocket, the authors believe it’s reasonable to expect the company to deliver launch prices close to $200 per kilogram by 2035.

What will it take to do that? Based on the amount of payload launched by the Falcon 9, they think a similar cost-reduction trajectory will require Starship to fly 370,000 tons of payload into orbit. That’s something that would take it about 1,800 launches over the next ten years, or 180 a year—and that’s if it can fly 200 metric tons on each mission.

That’s a big ask for a vehicle that has never flown more than five times in a year. SpaceX predicts the company will be flying far more than that—Elon Musk has suggested Starship could achieve an hourly flight rate in 2029, for example, but Musk says a lot of things.

The good news, at least, in Google’s updated research is that it seems likely that its chips will survive the radiation of space. The company had to redo tests blasting the chips in a particle accelerator after they realized the configuration of the chips provided more shielding than they would actually experience. This produced slightly more errors in the chip’s logic circuitry, but the company is still confident its chips can handle large inference workloads in orbit for the five-year lifespan of a satellite.

“The error rate is very low if you’re thinking about typical inference operations, right? Like one in a million,” Beals said. “On the other hand, it was already problematic for doing, say, some mega-scale training run where you’re going to have many thousands of chips running for months.”