진화를 학습 데이터로, Basecamp Research의 도전

Inside Basecamp Research, the AI startup turning evolution into training data Maximilian Schreiner View the LinkedIn Profile of Maximilian Schreiner Sep 23, 2026 Basecamp Research / GPT-Image-2 prompted by THE DECODER Key Points London-based Basecamp Research has raised $140 million from investors including Nvidia and Anthropic's Anthology Fund to build AI models for developing drugs and cell therapies. The models, called EDEN, train on DNA from microorganisms collected in environmental samples around the world. Basecamp uses them to design new antibiotics and biological tools that insert genes at targeted spots inside the body. Early lab work and mouse studies show that individual drug candidates work against multidrug-resistant bacteria. Human clinical trials still have to prove the therapies are safe and effective. Ask about this article… Search Basecamp Research has raised $140 million from investors including Nvidia and Anthropic's Anthology Fund. The London company trains AI models on genetic material from rainforests, oceans, and hot springs to design antibiotics and tools for cell therapies. In an interview with THE DECODER, CTO Philip Lorenz explains why biology is a far bigger problem for AI than language, and why good scores on paper don't guarantee good molecules. According to the company, investor S32 led the round. Nvidia, Anthropic's Anthology Fund, the NATO Innovation Fund, and Redalpine also took part, among others. Founded in 2020, Basecamp plans to use the money to keep developing its biological AI models, called EDEN , and to push its own therapy candidates toward clinical development. Cell therapies come first. These treatments genetically modify a patient's cells so they can fight cancer, for example. Basecamp wants to make that change directly inside the body. Ad To get there, the company looks to microorganisms from habitats researchers have barely studied. The models are supposed to learn patterns from their genomes that can be put to medical use. Ad "Given the complexity of biology, we just need orders of magnitude more data," Philip Lorenz, Basecamp's CTO, tells THE DECODER. What matters most, he says, is pairing that massive data collection with smaller, targeted experiments. There's still a lot of development work ahead before any of this becomes a treatment. So far, Basecamp has lab results and data from mice. That doesn't show whether its approach can produce safe and effective therapies in humans. Ad Why public genome databases can't carry biological AI The Wall Street Journal recently reported that pharma companies are pouring billions into AI, but solid evidence of noticeably higher success rates in clinical development is still scarce . Lorenz doesn't see that as a reason to doubt the technology. Scaling delivered big gains for language models very quickly. Biology is making progress too, he says, but the big open questions, like speeding up clinical trials, are far harder. As a problem, biology is "way, way, way more complicated and way larger." The data is also fundamentally different. Language models learn from huge text collections, while pharma companies often work with small, siloed datasets from individual studies. Ad Lorenz uses an estimate from Epoch AI to show how wide the gap is. It puts the upper limit of all existing words at roughly five quadrillion tokens. Basecamp estimates the number of nucleotides on Earth at 10 to the power of 37. "If you took a stack of cards, like poker cards, of 10 to the power of 37 cards, that stack would surround the observable universe a million times," Lorenz says. You don't need a dataset that big, he says, but you do need one far bigger than what exists today. Ad Public genome databases also paint an incomplete picture of nature. According to Basecamp's research paper on its BaseData database , about 68 percent of the sequence volume in the Sequence Read Archive comes from just five species. Humans alone account for about 54 percent. That focus makes sense for medical research. For a model meant to learn as many different biological processes as possible, though, Basecamp sees it as a limitation, since many other life forms barely show up in the data. "If you were to train LLM only on newspaper articles from 1975, it would be a really, really bad model," Lorenz said in a case study from Microsoft , the company's cloud partner. "That is kind of where we are in biology." So Basecamp collects its own samples with local research partners, from rainforest soil, volcanic ground, and the deep sea off Antarctica. According to the latest funding announcement, the network now spans more than 30 countries and all seven continents. Microsoft puts the number of participating organizations at 208 across 31 countries. At the time of the interview, the dataset held about 15 trillion tokens, according to Lorenz. That already puts it in the same range as the text datasets used to train models like Claude or GPT. Here, though, a token is a single DNA building block. The AI doesn't process words like a chatbot does. It processes strings of characters that describe genetic material. According to Microsoft, the first EDEN generation trained on 9.7 trillion DNA building blocks from more than a million newly sequenced species. Basecamp ran the training with Microsoft researchers on Azure, and the compute reportedly matched GPT-4's. OpenAI has never officially disclosed that figure, however. Over the next year and a half, the dataset is supposed to grow roughly a hundredfold, passing one quadrillion tokens. The framework for that is the Trillion Gene Atlas announced in March, in which Basecamp, Anthropic, Nvidia, PacBio, and Ultima Genomics plan to assemble genetic data on the scale of one trillion genes. In its latest funding announcement, Basecamp already calls the Trillion Gene Atlas the training foundation for its EDEN models. It doesn't say how far the planned expansion has gotten. Bacterial arms races offer a blueprint for new drugs Lorenz sees evolution, which drives all biological processes, as the link between environmental samples and medicine. Whether a bacterium in a hot spring adapts to heat or a cancer cell spreads, similar selective pressures are at work. The genomes of humans, mammals, and nearly all vertebrates are largely sequenced, while the rest of biodiversity has barely been cataloged. Many drugs also originally came from plants, bacteria, and fungi. He points to competing microorganisms as a concrete example. Some of the most powerful therapeutic molecules come from "biological warfare" between organisms, he says. When a bacterial species tries to dominate an ecosystem, it sometimes develops substances that inhibit or kill rival species, securing food and habitat for itself. This happens billions of times in countless places on Earth, especially where nutrients are scarce. In medicine, such substances can be useful as antibiotics. In the Microsoft case study, Lorenz names phages as another example. These viruses infect bacteria and inject their DNA into the cells. That back-and-forth produced tools that can be used for gene editing. Basecamp wants to learn from a wide range of these evolved solutions. The models aren't just supposed to rediscover known compounds. They're meant to design new candidates from the patterns they've learned. To do this, the company records the chemical, physical, and ecological conditions at each site along with the genetic material. It also focuses on long, continuous stretches of DNA. These show which genes sit next to each other and might work together. Short, isolated fragments often lose that context. An AI-designed antibiotic clears its first animal test Lorenz sees EDEN's antibiotic designs as early proof of medical value. "We prompt on a pathogen and then the model designs an antibiotic that kills it," he says. In a research paper on the EDEN model family that hasn't been peer-reviewed yet, the authors report on a small, curated set of