The Real AI Race Isn’t Model vs. Model Anymore. Here’s Where Poolside Actually Fits.

The AI Founder Who Lost a $2 Billion Round, Then Shipped Anyway

Eiso Kant spent a decade building the unglamorous infrastructure of software before co-founding Poolside. Four months after his company’s biggest funding round collapsed, he’s the one telling AI’s most honest story about what wins the race.

The setback nobody’s talking about, including him

In October 2025, Poolside announced Project Horizon: a 2-gigawatt AI data center on 568 acres of Texas ranchland, built with CoreWeave as anchor tenant and backed by a parallel $2 billion Series C targeting a $14 billion valuation, with Nvidia’s venture arm slated to anchor up to $1 billion of it. It was the kind of announcement that signals a company has arrived.

By April 2026, the round had collapsed. CoreWeave terminated its 15-year, 250-megawatt lease in late March, citing a new $8.5 billion loan facility that let it shift toward flexible, multi-tenant leasing instead of fixed commitments to a single partner. Nvidia declined to lead rescue financing. Poolside was left seeking new data-center partners for a campus it no longer had an anchor tenant for.

Four months later, on July 22, 2026, Poolside shipped Laguna S 2.1, an open-weight coding model that beats systems many times its size, and its co-founder was on a podcast the next day talking almost entirely about engineering process, not the round or the lease. Neither one is part of the story he’s telling right now. That says something about how this founder operates that his public statements alone wouldn’t tell you.

Who Eiso Kant is before Poolside

Kant is a Dutch software entrepreneur who studied business administration at IE University in Madrid, then spent more than a decade building companies most people never heard of, each one a step closer to the problem he’s working on now. He co-founded Tyba, a recruitment platform, in 2011. In 2015 he founded source{d}, described in his own bio as the world’s first company dedicated to applying AI to code, and open-sourced the infrastructure it used to train those models. When source{d} wound down in 2019, he started Athenian, an engineering-analytics platform, and ran it until 2023.

Three companies in a row, each one about instrumenting or improving how software gets built, before he ever touched a foundation model company. That’s the through-line. When Kant says the real bottleneck in AI is engineering and infrastructure, not research, it’s the same problem he’d already spent a decade circling.

He co-founded Poolside in May 2023 with Jason Warner, GitHub’s former CTO. The pitch from day one was that code is the shortest path to general intelligence: unlike most human knowledge, code comes with a built-in, machine-checkable feedback signal, you can run it and see if it works.

The funding story, in full

Poolside’s capital sequence: a $26 million seed in May 2023, a $126 million seed extension that August, and a $500 million Series B in October 2024 at a $3 billion post-money valuation, roughly $626 million raised before the Series C attempt. That’s a serious war chest, and it bought Poolside real infrastructure: a partnership with CoreWeave for 40,000-plus Nvidia GB300s, announced in October 2025 alongside Kant’s own framing of the company’s ambition: “we believe that to compete at the frontier, you have to own the full stack: from dirt to intelligence.”

The Series C and the Texas campus were supposed to be the next chapter of owning that full stack. They collapsed instead. What makes Kant’s story interesting right now is that the company kept building through a real setback and has something to show for it four months later.

What he’s selling: the Model Factory, not the model

Ask Kant what Poolside’s edge is and he doesn’t lead with benchmarks. He leads with a manufacturing metaphor. Per BigGo Finance’s writeup of his recent comments, Kant describes the company’s core internal metric this way: “The metric that we are optimizing for is the speed of an idea from a researcher to an experimental result that we can trust to then being part of the next model training.” He compares it directly to SpaceX’s rocket production line: the first rocket is hard, but the system that reliably produces the second, third, and hundredth one is the actual business.

Poolside calls this system the Model Factory, and per StartupHub.ai, it runs 10,000 to 20,000 experiments a month and took Laguna S 2.1 from the start of training to public release in under nine weeks. In a July 23 Latent Space appearance, Kant went deep on the specifics: streaming data directly into training instead of staging it, reproducible experimentation, low-precision compute, and agents that increasingly write the code, launch the jobs, evaluate the results, and modify the pipelines used to train the next model.

The claim underneath all of this, stated plainly in that same conversation: 90% of building a frontier AI lab is an engineering and infrastructure problem, not a research one. That’s a real, falsifiable position, and it cuts against the popular idea that the labs with the most PhDs or the most exotic research win. Kant’s bet is that the lab with the fastest, most reliable idea-to-model pipeline wins, and that pipeline is a systems-engineering achievement, not a research paper.

Why he wants more competitors, not fewer

Kant has said the same thing in slightly different words across multiple interviews this year, and he repeated it again on the July 23 podcast, per @poolsideai: “I’d rather live in a world that has 100 foundation model companies than a world that has five.” It’s a direct rejection of the consolidation narrative dominating the rest of the AI industry, where two or three closed labs increasingly control the frontier.

That philosophy shows up directly in Poolside’s product decisions. The company chose to release Laguna S 2.1 with open weights rather than lock it behind an API, and per Poolside’s own launch quote, Kant frames the model itself as proof of the method: “Laguna S 2.1 does the work of models several times its size because of how we build, not despite it.” At 118 billion total parameters with only 8 billion active per token, it’s small enough to run on a single Nvidia DGX Spark, and per VentureBeat, it lands in a size class no Western lab had released open weights into in 11 months, since OpenAI’s gpt-oss-120b the previous August. For a stretch of 2026, the open-weight coding-model conversation was effectively a conversation about Chinese labs. Kant’s answer to that gap was a shipped model.

How the people who’d actually run it responded

The most honest signal on a model release is what happens when people load it onto their own hardware, not the coverage on launch day. r/LocalLLaMA’s reaction thread opened with genuine enthusiasm: “Finally an interesting 120B contender,” with commenters immediately comparing quantization formats and noting Poolside shipped an NVFP4 version on day one, “actually exciting stuff.” That’s a different kind of validation than a benchmark chart, it’s practitioners deciding a release is worth their time within hours.

It wasn’t a flawless launch. A separate thread flagged a chat-template issue that needed a fix, and another traced a “thinking forever” looping bug to a quantization artifact rather than a core model problem. Real bugs, caught and discussed by real users within a day of release, is what an actual open-weight launch looks like, not a polished announcement with nothing underneath it.

What this says about how he operates

Put it together and Kant’s last 30 days reads less like a comeback narrative and more like a founder who never stopped building through the part most companies would have paused for. A company that just watched a $2 billion round and a marquee infrastructure partner disappear does not usually ship a genuinely competitive model eight weeks later and go on a podcast to talk about its manufacturing process instead of its problems. Kant’s entire career, three companies deep before Poolside even existed, has been about building the unglamorous systems underneath software work. Right now, that instinct is the whole company’s advantage: while the industry argues about which lab has the best model this month, Kant is arguing that the factory that reliably produces the next one, and the one after that, is the thing worth building.

By Anthony Batt — 20+ years building software and digital media products at scale. Podcasting host at Future-Proof Podcast by CO/AI.