Here’s one metric for tracking SpaceX’s IPO later this week: The company has changed the venture industry’s perspective on long-term, capital-intensive space so much that a talented founder with no space experience can fund a space data center company.

Orbital, a new firm that emerged in May from a16z’s startup accelerator program Speedrun with a $5 million seed round, is the latest company promising to do inference in space — just as soon as Starship is flying regularly. Other investors include Basis Set, Human Element, Wayfinder, Antler, Anti Fund, Ascent, Rubik, Zero Knowledge Ventures, LYVC, Feld Ventures, New Legacy, FNDR, UpHonest, and Asterisk.

Founder and CEO Euwyn Poon previously founded e-scooter company Spin in 2017 and sold it to Ford a year later, joining the automotive giant. When he was ready to start a new company, a16z’s Speedrun was eager to get on board, according to partner Andrew Chen, who told TechCrunch that Poon worked through several ideas before landing on space data centers.

You’re familiar with the pitch. There’s insatiable demand for AI compute, and deploying it is slow going on Earth. Why not head to space for limitless sunshine and limited environmental reviews? The main problem is the brutal economics of launching stuff into orbit, which currently leaves the business case unable to close.

Orbital, like many of it competitors, is betting on SpaceX figuring out its Starship rocket and offering it to commercial customers. “We will get to full scale when Starship comes online,” Poon explained. The price of the Falcon 9, the current state of the art, “makes this not economically feasible.”

For now, Poon and company — which includes about a dozen folks in Los Angeles, with experience at Amazon LEO, SpaceX, and Northrop Grumman — are working toward a demo flight that will see the company fly an Nvidia Blackwell chip on a partner’s satellite to test Orbital’s radiation shielding and thermal management tech. In 2028, the company hopes to launch its first data-processing spacecraft with Nvidia’s Space-1 Vera Rubin-class GPUs.

At that point, the company wants to start doing piece-wise inference work, which would allow it to generate revenue with each satellite launched. That’s a similar path to rival data center startup Starcloud, which already has a GPU in orbit and plans to launch several more to generate income until Starship enables them to deploy their full constellation.

Orbital’s goal is to deploy 10,000 satellites that provide a distributed gigawatt of computing power, with each satellite providing 100 kW of power. For comparison, Elon Musk said SpaceX expects its AI satellites to produce up to 150 kW, and Starcloud expects to field larger 200 kW-rated spacecraft to run chips.

Some companies are too impatient to wait for Starship. Cowboy Space Company, another space data center startup backed by a16z, recently decided to start building its own rockets. Jeff Bezos’ space company Blue Origin also announced plans to launch data centers into space using its New Glenn launch vehicle.

Poon is confident that the breadth of AI demand will allow many companies to succeed. “There’s so many lanes for companies in our space to pursue,” he told TechCrunch, before rattling off an array of choices that included companies pursuing different AI workloads, designs, and concepts of what a space data center looks like.

Chen said that Poon’s experience scaling up a company that deployed 250,000 scooters across 100 cities shows he can manage the tricky task of building an aerospace company. Over the long term, a project like this might take a decade and $5 billion or more, but Chen said venture firms are more comfortable with timelines like that.

“This kind of thing would have sounded crazy 10 years ago when we were all building mobile apps,” he said. “Starting it in 2026 just lets you tap into all the energy and excitement that’s happening in the capital markets.”

Poon found his way into the space data center business by a circuitous route. After leaving Ford, he bought a Nvidia A100 on a lark, co-locating it in a Santa Clara data center and serving open-weight models. That firsthand experience convinced him the value in delivering compute in the era of AI.

Now he’s just got to put a couple thousand GPUs in space.

Europe’s fast-growing vibe-coding startup, Lovable, tells TechCrunch it has surpassed $500 million in annualized revenue run rate.

Lovable last discussed its revenue in February, when the company said it crossed $400 million. In August, 2024, Lovable said it could hit $1 billion in annualized revenue within 12 months. It may not be on track to double that figure by summer, but it is still reporting jaw-dropping growth; the company, founded in late 2023, hasn’t yet hit its three-year anniversary.

The company also claims it has been used to build over 50 million projects and says usage has accelerated to one million new projects a week. According to a survey of those projects that run on the company’s blog, Lovable says its users are primarily non-technical, yet are increasingly building software they intend to monetize or use in their businesses.

Its users are founders, designers, and salespeople building websites and e-commerce storefronts, as well as internal tools like CRMs, inventory systems, and HR platforms, the company says.

That list tells a story. AI vibe-coding platforms have been seen as a threat to legacy SaaS software. Why buy expensive annual contracts when you can just vibe code it yourself? Lovable’s survey appears to offer some data that this is indeed happening. Of course, Lovable — and therefore most of the projects built on it — isn’t old enough to answer the harder question about vibe-coded software: Will such an approach prove short-lived? That’s because it’s not the initial building part that’s the problem — it’s the maintaining part.

Software operates almost like a living organism: Even well-written, well-designed code that isn’t AI slop runs atop an ever-shifting stack of dependencies, third-party services, and infrastructure — all of which is constantly being updated, which means end-user software is always breaking. That’s why so many companies choose to buy instead of build. They want others to be responsible for keeping it running. We’ll have to see if Lovable and other vibe coders will transparently report abandoned projects as their platforms mature — aka the not-as-flattering stuff. If those abandonment rates are low, that will be the true indication that the so-called SaaSpocalypse is here and here to stay.

With Harvey and Legora burning through eight-figure funding rounds, legal tools have proven to be one of the fastest-growing and most hotly contested verticals among AI startups. But while those tools focus on private practice, some startups believe there’s still plenty of the legal market that isn’t being served.

Sandstone, which announced $30 million in Series A funding on Tuesday, is focused on an overlooked slice of the legal space, focusing on the tangle of overlapping tasks and systems facing in-house legal teams.

The Series A was led by Lightspeed Venture Partners, with participation from existing investors at Mantis VC, SV Angel, Operator Partners, Kearny Jackson, Daybreak Ventures, Litquidity Ventures, and others. The Series A comes just six months after a $10 million seed round in January, which was led by Sequoia.

As the founders describe it, Sandstone’s initial user base will be the legal departments at small and mid-sized businesses.

“They open up their laptop in the morning, they see all the work that’s come in through different intake channels, whether that’s Slack messages, emails, Jira,” co-founder and chief operating officer Jarryd Strydom told TechCrunch. “AI helps them route and triage that work appropriately, and then they can build custom workflows on top of our platform to actually execute work, whether that’s drafting, reviewing, or providing legal analysis.”

The result has little in common with legal reasoning systems like Harvey and Legora. Instead, Sandstone focuses on relationship management and workflow automation, both tuned to the unique demands of in-house legal work. As Strydom sees it, the focus on in-house legal departments allows Sandstone to provide value where more generalized AI deployments often flounder.

“One of the convictions of Lightspeed was that they really believe in highly specialized vertical AI,” Strydom said, “because it takes a granular understanding of workflows to really nail down how AI can help.”

Sandstone will also face heated competition from frontier AI labs, which are increasingly turning their attention to the legal space. Anthropic has been steadily expanding its Claude for Legal offering, adding new tools in May for case law searches and deposition prep.