When Allbirds pivoted to AI in April, it felt like a joke from Silicon Valley breaking free of the TV: The direct-to-consumer shoe purveyor whose flimsy kicks helped define what we’ll loosely call Silicon Valley style had discovered a new trend to chase.
The move was right out of the meme stock playbook written by Gamestop: Take a troubled public company, latch onto the hottest fad, and reap the rewards of a rising stock price as retail investors piled in.
Well, it worked. The company sold its shoe business for $43 million, raised another $100 million from the stock market, and now it’s called Smartbird.
Now, Nadia Carlsten has to make it work. A former AWS executive with an engineering PhD, Carlsten most recently led the European compute company DCAI before she began yesterday as Smartbird’s CEO.
“We’re going to be recruiting a brand new team for the AI business, and we’re going to be getting an office,” Carlsten told TechCrunch from Amsterdam. “The shoe business has officially closed as of yesterday, so that’s all done…The first task that I’m tackling right now is rounding up the leadership team, looking for somebody to lead infrastructure operations, for example.”
Call it a startup with a sole founder and a very large seed round. What’s next is less clear.
Smartbird aims to be an AI infrastructure provider, latching on to the seemingly bottomless demand for compute to train and run deep learning models. But unlike neoclouds, which relentlessly arbitrage the price of chips against the cost of GPU time or inference tokens, Carlsten will be aiming at more carefully managed deployments. The ideal Smartbird customers need direct control over the servers running their models — typically for political or business-model reasons — and value data sovereignty over the scalability of the public cloud.
Carlsten couldn’t yet estimate the size of that market, and argued that it was fairly nascent, since many companies are still just piloting AI tools. At DCAI, she worked with Novo Nordisk and other European firms who take a special interest in data sovereignty or operate bespoke models—”we certainly have anybody that’s within the pharmaceutical industry, energy industry, financial, the public sector,” she said.
To Carlsten’s view, that means Smartbird isn’t competing with hyperscalers or neoclouds, but with internal company projects. Still, there are established companies in this space—Hewlett Packard offers a single-tenant managed AI compute service, as does Equinix, the data center giant.
It’s real business model, but it’s not clear if it has the same growth potential as the cloud services, where expansion is the be-all, end-all. Carlsten said she expects to have compute clusters deployed for several customers by the end of the year. Other startups, like the inference cloud General Compute, have bigger ambitions—the company announced a $300 billion chip order when it came out of stealth last month.
Carlsten says she doesn’t need big chip commitments to realize Smartbird’s vision, because her potential customers needs sit in the range of hundreds to thousands of chips—it’s “not about large scales and huge numbers of GPUs, they’re more about agility of these clusters, and more about having control of the infrastructure stack.”
Smartbird is also unlikely to compete with rivals on price, since cloud services go to great lengths to optimize chip usage 24 hours a day to offer the cheapest compute, though Carlsten suspects that companies with specialized workflows will be able to work more efficiently with their own servers.
Demand for AI infrastructure is a powerful force in the market, driving up the stock prices for chipmakers, cloud providers, and energy companies, even convincing investors that orbital data centers are a feasible idea. But Carlsten insists that Allbird’s transition was carefully thought through.
“It wasn’t, ‘Let’s just do AI, because it’s AI, and it’s hot,’” Carlsten, who will be paid a $700,000 annual salary
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