Artificial Intelligence

Why biological data is becoming the real battleground in AI drug discovery

Published

2 months ago

August 11, 2026

The $110 million bet on cells, not just algorithms

When GSK expanded its partnership with London-based Relation Therapeutics in a deal worth up to $110 million, the pharma giant wasn’t just buying another AI platform. It was paying for something far scarcer: high-quality biological data.

The collaboration centers on generating large-scale datasets that measure how human cells respond to genetic changes and drug interventions. That data will train AI models to spot potential drug targets, including those within Relation’s MORGAN platform.

It’s a telling shift. For years, the buzz in drug discovery has been about smarter algorithms. Now, the bottleneck is moving to the fuel that powers them.

What Relation Therapeutics actually does

Relation calls its approach Lab-in-the-Loop. It’s a cycle: run lab experiments, feed the results into computational models, use those models to design the next experiment. The company handles tissue profiling, single-cell and spatial transcriptomics, sequencing, and target validation. Machine learning sits at the center, guiding target identification and experimental design.

Its perturbation experiments are particularly interesting. These measure how genetic tweaks alter cellular characteristics tied to disease. The results get analyzed alongside genetic and patient-derived data, creating a richer picture than any single dataset could offer.

This isn’t theoretical. Relation’s Osteomics project is a proprietary functional single-cell bone atlas, built from patient samples and combining single-cell and spatial omics with imaging, genomics, proteomics, and clinical phenotypes. It’s already being used to investigate osteoporosis biology and identify patient subgroups, with hospitals in the UK and Australia involved.

Why bigger datasets don’t automatically mean better AI

Here’s where things get counterintuitive. You might assume more data always helps. A June 2025 study in Nature Methods suggests otherwise.

Researchers trained 400 single-cell foundation models on a corpus of 22.2 million cells, evaluating them across 6,400 experiments. The result? Models hit performance plateaus after training on only a fraction of the available data. Unlike large language models, these systems didn’t show clear scaling laws where more data consistently led to better results.

The study concluded that model capacity, dataset size, and compute need to be balanced—not just cranked up together. Adding more biological data didn’t reliably produce better models.

A separate 2025 study in Genome Biology evaluated Geneformer and scGPT, two well-known single-cell foundation models. Neither consistently outperformed simpler approaches. The researchers also flagged batch effects and warned against assuming larger pretrained models automatically yield better biological representations.

Quality control is the real challenge

The problem isn’t just volume. It’s consistency. A 2025 review in Experimental & Molecular Medicine noted that public repositories like CZ CELLxGENE, the Human Cell Atlas, and NCBI Gene Expression Omnibus offer vast amounts of single-cell data—CZ CELLxGENE alone has over 100 million standardized cells.

But that data comes from hundreds of labs, each with its own sampling methods, sequencing protocols, and processing pipelines. Technical noise and artifacts are everywhere. Dataset overlap is another headache: the same cells can appear in multiple resources, giving them outsized influence during training and creating data-leakage risks when training and test sets overlap.

The review’s conclusion was blunt: assembling a high-quality, non-redundant dataset matters just as much as model architecture.

Pharma’s new playbook: specialized data as a strategic asset

This is why companies like GSK are paying premium prices for proprietary data. A 2025 Nature Biotechnology analysis of AI-focused biopharma deals identified specialized dataset providers as a key trend, alongside larger upfront payments and new therapeutic modalities.

The analysis pointed to several examples:

GSK’s separate $37.5 million agreement with Ochre Bio for human liver single-cell and perfused-organ data
AstraZeneca and Pathos AI’s $200 million deal with Tempus in 2025, covering de-identified clinical, genomic, and imaging data from over 150,000 patients
Relation’s own Osteomics atlas, built specifically for osteoporosis research

These deals reflect a simple reality: high-quality, disease-specific datasets are becoming a critical input for causal and generative machine-learning models. Public data has its place, but it’s not enough.

The data bottleneck won’t disappear anytime soon

A Nature research highlight on federated learning in pharma called limited access to suitable training data a major bottleneck for AI applications. Companies face restrictions on sharing proprietary information, and even when they want to collaborate, the data infrastructure often isn’t there.

That’s why AI-biopharma agreements take so many forms. Some focus on accessing AI platforms. Others cover joint development or data licensing. The GSK–Relation deal does both: it funds data generation and model development, with Relation producing human cellular datasets and using them to train target-identification models.

The message is clear. In AI drug discovery, the algorithm is no longer the differentiator. The data is. And the companies that control the best biological datasets—not the biggest ones—will likely lead the next wave of discoveries.

For more on how AI is reshaping pharma, check out how AI is shortening drug discovery timelines in China and AI foundation models in biomedicine.

Artificial Intelligence

XDOF, three months out of stealth, is already closing in on a $1.2B Series B

Published

4 weeks ago

September 6, 2026

Social Media Admin

From stealth to unicorn talk in record time

Three months. That’s how long XDOF has been out in the open. And already, the robotics data startup is in late-stage conversations to raise a Series B at a valuation hovering around $1.2 billion, according to multiple sources familiar with the negotiations. The round would be led by 8VC.

Not bad for a company that didn’t even exist publicly until June.

XDOF was co-founded in 2024 by UC Berkeley researchers Philipp Wu (CEO) and Fred Shentu (CTO). Their origin story traces back to a research project called GELLO — a low-cost teleoperation system that lets a human operator control a robotic arm from a distance. The goal? Generate training data for robots. That work produced an influential paper in robotics and, eventually, a company.

The startup’s Series A, a $70 million round announced in June, drew participation from Thrive Capital, Andreessen Horowitz, Lux, and Spark Capital. At the time, XDOF wasn’t planning to raise again so soon. But the market had other ideas.

Why investors are knocking on XDOF’s door

The reason for the sudden interest? Growth. Real, measurable growth.

XDOF’s annualized revenue is approaching $50 million, sources say. That kind of traction, so soon after a Series A, tends to make venture capitalists sit up and take notice. It also tends to make them pick up the phone.

“They weren’t out raising,” one person familiar with the situation told TechCrunch. “The VCs came to them.”

Terms aren’t final, and the total capital being raised remains unclear. TechCrunch couldn’t confirm whether the $1.2 billion valuation includes the new funding or sits on top of it. Both XDOF and 8VC declined to comment.

The Scale AI for physical robots

XDOF’s pitch is straightforward: it builds the data pipelines, collection tools, and annotation systems that frontier AI labs and robotics companies would rather not build themselves. Think of it as an outsourced data-supply chain for the robotics industry.

Investors describe XDOF as the Scale AI or Mercor of physical robotics — a nod to the data-labeling giants that powered the AI boom. The comparison makes sense. Large language models trained on the entire internet. Physical robots? They don’t have that luxury. There’s no massive, ready-made dataset of real-world robot interactions sitting online. That scarcity makes data collection the critical bottleneck on the road to general-purpose machines.

Wu felt that bottleneck firsthand as a PhD student. His research on how robots learn from large datasets kept hitting the same wall: “large-scale data to work with” simply didn’t exist, he told TechCrunch in June.

Building the ABC dataset

XDOF is tackling that problem head-on. The startup is partnering with UC Berkeley’s AI Research lab to release what it believes is the largest collection of high-quality robot training data ever assembled. The dataset is called ABC.

Collecting that data requires a hybrid approach. XDOF combines remote robot teleoperation with human collectors who wear sensors to record everyday tasks. Think folding clothes. Flattening boxes. The mundane, physical chores that robots still struggle to master.

The company plans to hire and train teams of data collectors around the world. Two main roles are emerging:

Teleoperators who steer robots remotely to demonstrate tasks
Egocentric operators who wear body sensors to capture natural movement data

Early traction and the competitive landscape

XDOF has already signed up 20 customers, including several frontier AI labs, according to previous statements to TechCrunch. That customer base, combined with the revenue trajectory, helps explain the valuation chatter.

But XDOF isn’t alone in this niche. Other startups chasing real-world data for robot training include Mecka AI. And the human-data platforms that started with LLMs — like Scale AI and Micro1 — are expanding beyond text and images into physical domains.

The race to build the data infrastructure for physical AI is heating up. Whoever wins it will effectively control the fuel supply for the next generation of robots. That’s a position worth paying up for.

Whether the $1.2 billion valuation holds remains to be seen. Deals at this stage can shift. But the fact that XDOF is even in this conversation — three months after emerging from stealth — says something about the demand for what it’s building.

For more on how data is shaping the future of AI, check out AI data labeling trends and robotics funding rounds in 2024.

Artificial Intelligence

New York City Pulls AI From Younger Classrooms—Here’s Why It Matters

Published

4 weeks ago

September 6, 2026

Social Media Admin

New York City Just Hit Pause on AI in Classrooms

New York City Public Schools is drawing a hard line: no generative AI for students from 2-K through eighth grade during the 2026-2027 school year. That’s over half a million kids walking into classrooms next week without access to AI-powered tools.

The district says it will remove software with student-facing AI features and block AI companion chatbots. High schoolers? They’re exempt. This isn’t a blanket ban—it’s a targeted move to decide when kids should actually start using the technology.

Mayor Zohran Mamdani put it bluntly: “The tech industry wants us to believe that AI-powered early education is not only inevitable, but necessary. We do not see it that way.”

Why NYC Is Worried About AI for Younger Students

The fear isn’t just that a kid will ask ChatGPT to write an essay. City officials want younger students to build core skills—critical thinking, creativity, communication—without leaning on AI as a crutch. They’re also pushing for stronger human connections in classrooms, not another screen.

Schools Chancellor Kamar Samuels said the city refuses to assume that innovation automatically equals more technology in front of students. It’s a deliberate slowdown, and it follows last year’s bell-to-bell cellphone ban that already limits device use during school hours.

What Happens When Kids Reach High School?

AI doesn’t vanish once students hit ninth grade. Instead, the district plans to roll out AI literacy classes twice a year. The goal? Teach teenagers how to think critically about the technology before they become dependent on it.

That’s a different approach from just saying no. It’s about timing—letting younger minds develop without AI, then giving older students the tools to question it.

The National Battle Over AI in Education

NYC’s decision sits at the center of a much bigger fight. The White House has pushed educators to embrace AI responsibly. Some teachers already use it to craft lesson plans, give feedback, or break down tough subjects. But not everyone’s on board.

The Department of Health and Human Services recently gathered childhood experts to talk about excessive screen time. Officials have also called for tougher safeguards around social media and AI. The message? Kids are spending too much time in front of screens, and AI might make it worse.

A Bold Experiment With Zero AI

Here’s what makes NYC’s move so interesting: instead of asking how much AI younger students should use, the largest school district in the country is starting with none. For one academic year, they’re testing whether classrooms are better off with AI kept outside the door.

That’s a radical stance, and it’s not without critics. Some educators argue AI can personalize learning or help struggling students catch up. But NYC is betting that a year without AI will reveal what kids actually need—not what tech companies think they need.

What This Means for Parents and Teachers

If you’re a parent in NYC, expect changes. AI-based apps may disappear from your child’s school day. Teachers will need to plan lessons without generative AI tools. And students in grades 2-K through 8 will rely more on traditional methods—paper, pencils, and human interaction.

For teachers elsewhere, this could be a signal. NYC is the biggest district to take this stance, and its findings could shape policies nationwide. The next year will be watched closely by educators, policymakers, and tech giants alike.

What Happens Next?

The district will spend the year studying how generative AI affects students before deciding what comes next. That research could lead to a permanent ban, a partial rollout, or something entirely different.

For now, NYC is making a statement: childhood shouldn’t be an AI beta test. Whether that’s the right call or a step backward, we’ll know more in 2027. Until then, the debate over AI in schools just got a lot more interesting.

If you’re curious about how AI is shaping other areas, check out our take on AI in education trends or classroom technology policies.

Artificial Intelligence

Meta’s Muse Spark 1.3 takes on GPT-5.6 and Claude — but can it really win?

Published

4 weeks ago

September 5, 2026

Social Media Admin

Meta just fired a serious shot in the AI arms race

The company quietly unleashed Muse Spark 1.3, its most advanced AI model to date, and it’s aiming straight at the top dogs. Developers can already access and pay for the update, which Meta says represents a massive leap forward in performance.

It won’t stay confined to the developer sandbox for long. Over the coming weeks, the model will roll out across Instagram, Facebook, and the Meta AI assistant — putting it in front of billions of everyday users.

What makes Muse Spark 1.3 actually different?

Meta’s Chief AI Officer, Alexandr Wang, didn’t mince words. He called this “the biggest jump so far on model performance,” pointing to serious gains in two areas: coding and agentic tasks — the kind where the AI acts on your behalf rather than just answering questions.

But here’s the catch. Wang told Bloomberg that Muse Spark 1.3 is competitive with Anthropic’s Claude Fable 5.1 and even beats OpenAI’s GPT-5.6 Sol at coding. That’s a bold claim, especially with OpenAI’s upcoming Astra model lurking in the wings.

Take those comparisons with a grain of salt, though. Benchmarks can be gamed, and a model that crushes one test might stumble on a totally different task. Real-world performance is what actually matters.

Efficiency gains under the hood

Wang broke down a few upgrades that set Muse Spark 1.3 apart:

25% fewer tokens needed to complete the same job — meaning lower costs and faster responses
Multi-workflow handling — it can juggle several tasks at once instead of forcing separate sessions
Better context retention across long, complicated instructions
Self-awareness of limits — the model now pauses to ask for clarification before taking any irreversible action

That last point is quietly important. AI that knows when it doesn’t know is a big step toward trustworthiness, especially for agentic use cases.

Pricing stays flat, adoption explodes

Here’s something developers will appreciate: Meta isn’t raising prices. Muse Spark 1.3 costs the same as its predecessor, Muse Spark 1.2. That’s a smart move when rivals are hiking rates.

Wang told Bloomberg that adoption on Meta’s developer platform has been strong — some users are burning through trillions of tokens every week. Those numbers suggest real usage, not just hype.

What about open-source fans? Meta hasn’t decided whether it will release the model’s weights — the blueprint that lets outside developers build on top of it. The older Muse Spark 1.2 weights are still headed for release, but the new model’s future remains unclear.

The bigger picture: Meta’s spending spree continues

Meta is still pouring billions into AI infrastructure, and Muse Spark 1.3 is the clearest signal yet that the company believes it’s closing the gap with OpenAI and Anthropic. Whether that’s true or just corporate bravado will play out in the benchmarks and real-world deployments over the coming months.

For now, the model is available to developers, and the app rollout is imminent. If you’re building on Meta AI tools, this update is worth a serious look. And if you’re just a curious user, you’ll likely meet Muse Spark 1.3 in your Instagram feed sooner than you think.

One thing’s certain: the AI race just got a lot more interesting.