Tech
AI is spitting out more potential drugs than ever. This start-up wants to figure out which ones matter.
AI’s biggest impact in science is Google DeepMind’s use of a deep learning model to predict the complex structures of proteins — the molecules that drive virtually every process in living cells.
But as AI models continue to spit out more candidates for potential treatments, there’s an emerging bottleneck: actually characterizing all those candidates in practice, for testing and mass production.
That’s the goal of 10x Science, a startup founded in December 2025 that announced a $4.8 million seed round today, led by Initialized Capital and with backing from Y Combinator, Civilization Ventures, and Founder Factor. Its three founders are David Roberts and Andrew Reiter, experienced biochemists, and Vishnu Tejas, a serial founder with expertise in computer science and AI models.
“When biopharma tries to create a drug candidate, they have all of these really nice prediction tools,” Roberts told TechCrunch. “You can add as many candidates as you want to the top of the funnel, but they all have to pass through this characterization process. Everything needs to be measured.”
Understanding the structure of proteins is key for researchers developing biologic drugs, which are produced in living cells and use sophisticated design to specifically target diseases and conditions. For example, they can be designed to target specific cells, like Keytruda, a popular drug sold by Merck that helps the immune system identify and attack cancers.
10x’s three founders worked together in the Stanford lab of Nobel laureate Dr. Carolyn Bertozzi, where they studied the interactions between cancer cells and the immune system, and were frustrated by their inability to understand precisely what was happening on a molecular level.
The most accurate way to assess molecules is through a complex technique called mass spectrometry, a way of determining their atomic structure by measuring them in an electric field. The relatively new technique generates complex data that requires significant expertise to interpret, and analyzing it takes up a lot of time.
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10x’s platform combines deterministic algorithms rooted in chemistry and biology with AI agents that can interpret that data. The team had to do significant work to train the models on spectrometry data and make its analyses traceable, a key requirement for a tool that will be used to help companies achieve regulatory compliance.
Matthew Crawford is a scientist at Rilas Technologies, a firm that runs chemical analyses for other companies — saving clients like biotech startups from having to invest several million dollars in their own spectrometry equipment and the experts to operate it. Crawford has been using the 10x Science platform for several weeks and says it is speeding up his work.
Crawford said the model surprised him with its ability to explain its conclusions, find the right data for analyses on its own, and adapt to evaluating different kinds of molecules. While some AI tools he has experimented with in the past over-promised or suffered accuracy issues, he says this one makes reasonable assumptions, something he attributes to the deep domain expertise of its creators.
“I ran a particular protein through it, and it just kind of figured out, from what I named the file, what the protein probably was,” Crawford said. “It then searched databases online for the sequence for that protein, so I didn’t have to program in the sequence.”
10x executives say they’re also working with multiple major pharmaceutical companies, as well as academic researchers. The plan is to use this seed funding to hire more engineers and continue to refine the model and offer it to new customers. If they are able to gain traction characterizing proteins, Roberts hopes the company will expand to offer a new kind of understanding of biology, combining protein structure with other data about cells.
“The deeper thing behind what we’re building is actually a new way to define molecular intelligence,” Roberts said.
For its investors, 10x offers a useful way into the biotech space that isn’t dependent on a specific drug succeeding and winning regulatory approval. If the company works out the way its founders hope, it will become an important tool for drug development, whether or not the eventual products succeed in the marketplace.
“This is a SaaS platform that pharma has to pay for, every single month, to go through all of these potential candidates,” Zoe Perret, a partner at Initialized, said. She’s counting on the deep experience of the founders to protect the company from competitors; there simply aren’t that many people who understand these methods and the data they produce.
What the platform could do, Crawford says, is help unlock the techniques for researchers who could benefit from these methods but lack the time or resources to deploy them.
“Groups here are trying to make a new drug,” he told TechCrunch. “They just want to get a quick, simple answer out of mass spec, and then it opens up a whole can of worms. This software is going to help keep that can of worms closed and just get them the answer they actually need to then do the next thing in their research.”
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Tech
Exclusive: Google deepens Thinking Machines Lab ties with new multi-billion-dollar deal
Former OpenAI executive Mira Murati’s startup, Thinking Machines Lab, has signed a new multi-billion-dollar agreement to expand its use of Google Cloud’s AI infrastructure, including systems powered by Nvidia’s latest GPUs, TechCrunch has exclusively learned.
The deal is valued in the single-digit billions, according to a source familiar with the matter, and includes access to Google’s latest AI systems built atop Nvidia’s new GB300 chips, alongside infrastructure services to support model training and deployment.
Google has been actively striking a number of cloud deals with AI developers as it aims to wrap together its AI computing offerings with other cloud services like storage, a Kubernetes engine, and Spanner, its database product. Earlier this month, Anthropic signed an agreement with Google and Broadcom for multiple gigawatts of tensor processing unit (TPUs) capacity (these are Google’s custom-designed AI chips for machine learning workloads).
But the competition is fierce. Just this week, Anthropic also signed a new agreement with Amazon to secure up to 5 gigawatts of capacity for training and deploying Claude.
Earlier this year, Thinking Machines partnered with Nvidia in a deal that included an investment from the chipmaker. But this is the first time the lab has struck a deal with a cloud services provider. The deal is not exclusive, so Thinking Machines may use multiple cloud providers over time, but it’s still a sign that Google is looking to lock in fast-growing frontier labs early.
Murati left her job as OpenAI’s chief technologist and founded Thinking Machines in February 2025. The company, which soon afterwards raised a $2 billion seed round at a $12 billion valuation, has remained highly secretive, but launched its first product in October. Dubbed Tinker, it’s a tool that automates the creation of custom frontier AI models.
Wednesday’s deal provided some insight into what Thinking Machines is developing. In a press release, Google noted that it can support the startup’s reinforcement learning workloads, which Tinker’s architecture relies on. Reinforcement learning is a training approach that has underpinned recent breakthroughs at labs, including DeepMind and OpenAI, and the scale of the Google Cloud deal reflects how computationally expensive that work can get.
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Thinking Machines is among the first Google Cloud customers to access its GB300-powered systems, which offer a 2X improvement in training and serving speed compared to prior-generation GPUs, per Google.
“Google Cloud got us running at record speed with the reliability we demand,” Myle Ott, a founding researcher at Thinking Machines, said in a statement.
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Tech
The most interesting startups showcased at Google Cloud Next 2026
Google Cloud Next is taking place this week in Las Vegas, and one clear message has emerged: Google wants AI startups on its cloud. To that end, it made several startup-related announcements.
The most significant is that the tech giant has earmarked a new $750 million budget to help its Cloud partners sell more AI agents to enterprises. This funding is available to partners ranging from startups to the big consulting firms. It can be used for costs like Gemini proof-of-concept projects, Google forward-deployed engineers, cloud credits, and deployment rebates.
Google also highlighted a long list of startups that are using Google Cloud, either newly signed or expanding their footprint. Among them are a few standout names:
Lovable is expanding its use of Google Cloud by launching a new coding agent through Google’s enterprise app marketplace. Lovable is the fast-growing vibe coding startup and was on a $400 million ARR track as of February, it said.
Notion, Silicon Valley’s favorite AI-infused document productivity app, most recently valued at about $11 billion, is using Gemini models to power its text and image generation features.
Gamma, an AI-powered PowerPoint killer recently valued at a $2.1 billion valuation, is using Google’s state-of-the-art image model Nano Banana 2 and other Google Cloud features.
Inferact, the commercial inference startup from the creators of the popular open-source project vLLM, is accessing Nvidia’s GPUs through Google Cloud, in addition to using the tech giant’s AI stack.
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ComfyUI, the popular open-source tool for creating AI-generated images and multimedia, also offers access to Nano Banana 2 and is using other Cloud features.
Other startups that received the Google Cloud shout-out this year include:
ChorusView, which makes AI-powered smart tags that track the condition and movement of goods in real time.
Emergent AI, a vibe coding platform.
ExaCare AI, which makes AI software for post-acute medical care facilities.
Insilica, which creates AI-generated regulatory-compliant chemical safety reports.
Optii, which makes AI-enhanced hotel operations software.
Parallel AI, which builds web search and research APIs built for AI agents.
Proximal Health, which makes AI-powered software that automates the insurance claims adjudication process.
Reducto, which does AI-powered document parsing.
Stord, which handles e-commerce fulfillment and parcel operations.
Stylitics, which makes AI image generation software for retailers for tasks like outfit styling and product bundles.
Temporal, a developer cloud environment built to prevent failures.
Vapi, which makes dev tools for building conversational voice agents.
Vurvey Labs, which conducts synthetic market research via AI agents.
Wand, an in-game assistant for single-player PC games.
Watershed, which makes software that helps enterprises report on and manage sustainability programs.
ZenBusiness, an all-in-one back-office tool for small businesses that includes an AI chat assistant.
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Tech
Duolingo is now giving free users access to advanced learning content
Duolingo announced on Wednesday that its advanced language learning content is now available for free across nine languages: English, Spanish, French, German, Italian, Portuguese, Japanese, Korean, and Chinese. Users can access this content through the web, iOS, and Android devices.
This advanced content is at the B2 level on the Common European Framework of Reference for Languages (CEFR), which is the international standard for language skills that schools and employers recognize. B2 level content refers to learning materials without translations, complex scenarios, and specialized vocabulary.
The new offering will include features like “Advanced Stories,” which helps with reading comprehension, and DuoRadio, a podcast-like audio experience for listening comprehension.
Now that Duolingo users can tap into this advanced learning content for free, they can level up their skills, whether that’s practicing for job interviews, prepping for studying abroad, or tackling complex news articles, films, and books without relying on translations.
The company says this positions it as the only free app to offer advanced-level learning across these nine languages at no cost. While competitors like Babbel and Busuu offer advanced courses, they typically require paid subscriptions. For instance, Busuu has some CEFR-aligned courses up to the B2 level, but the free version is pretty limited and doesn’t offer lessons like grammar explanations, so users need to pay for full access.
Previously, Duolingo only provided free courses that capped at A2 or B1 levels, mainly focusing on basic communication skills.
The company is positioning this free advanced learning offering as an enticing opportunity for job seekers, framing language learning as a practical pathway to improving employability in an increasingly global workforce.
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This comes at a time when the job market remains highly competitive and overall growth has slowed. Research from the American Council on the Teaching of Foreign Languages shows that learning a second language can raise someone’s employability by as much as 50%.
“Reaching job-ready proficiency in a new language used to be out of reach for most people,” Bozena Pajak, head of learning science at Duolingo, said in a statement. “It took years of expensive classes or immersive experiences that not everyone could access.”
Duolingo’s decision to offer advanced learning for free is also a strategy to increase its free user base. In its Q4 earnings report, the company stated that it has 52.7 million daily active users, demonstrating 30% growth compared to the previous year. This number is higher than its paid subscriber base, which stands at 12.2 million. However, Duolingo’s shares fell after the company projected that the year-over-year bookings growth rate for Q2 2026 is expected to experience a slight decline.
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