I’ve been thinking a lot lately about the difference between building things in public versus building things in silos. It’s one of those distinctions that seems simple on the surface but actually shapes everything about how innovation happens in technology.
Google Research published something recently that crystallized this for me. Their philosophy around open science isn’t just about altruism or academic nicety. It’s infrastructure thinking. They’ve created an environment where fundamental research and applied product work coexist, where publishing findings isn’t seen as giving away competitive advantage but as a multiplier for impact. The distinction matters more than you might think.
The Transformer Moment and What Came After
When Google researchers released the Transformer architecture, they didn’t just publish a paper. They created a substrate. Something that thousands of teams could build upon simultaneously. The architecture didn’t reach its full potential because Google deployed it across their products, though they certainly did that. It reached its potential because researchers at OpenAI, Meta, Anthropic, and countless smaller labs could take that foundation and iterate on it in parallel.
That’s what open science actually looks like when it works. It’s not charity. It’s economics at scale.
What interests me is how deliberate this approach has become at the institutional level. Google Research doesn’t just tolerate open science. They’ve structured their entire operation around it. They maintain datasets and tools. They engage with university faculty. They run programs for next-generation researchers. This isn’t a side project. It’s how they’ve decided to do research.
The practical outcome is staggering. Over the last decade, their open-source technologies and datasets have empowered more than 250,000 researchers and developers worldwide. I’d argue that’s a more meaningful metric than any of their proprietary metrics could be.
Where the Real Work Happens
Here’s what most people miss about this approach. The breakthroughs aren’t happening in isolation anymore. When Sunbird AI, an African nonprofit, uses Google’s Open Buildings dataset to model energy needs across urban and rural communities, they’re not just applying existing technology. They’re validating it, stress-testing it against real-world constraints that the original creators might never have considered.
The All India Institute of Medical Sciences is using MedGemma to build outpatient triage and dermatology screening applications. That’s not just an adoption story. That’s a use case that will feed back into improvements to the model itself.
This is where AI becomes genuinely collaborative. The model improves because real users with real problems are putting it to work in contexts that matter. The dataset becomes better because people are using it to solve problems the creators didn’t anticipate.
The partnerships Google Research has built out speak to this. They’re working with the University of California Santa Cruz Genomics Institute, with Janelia Research Campus, with the Institute of Science and Technology Austria, with CSIRO in Australia, with AIIMS in India. These aren’t token collaborations. They’re deep partnerships where knowledge flows both directions.
The Infrastructure Play
I think what’s happening here is less about Google being altruistic and more about them recognizing something fundamental about how technology actually advances. Innovation isn’t a siloed event anymore. It can’t be. The problems we’re trying to solve in genomics, neuroscience, climate, medicine, these are too complex for any single institution to solve alone.
By creating the infrastructure for collaboration, by maintaining datasets and tools, by publishing consistently, Google Research is essentially saying we believe our competitive advantage lies in having better infrastructure, better talent, better ideas coming back to us because we’ve structured the system to make collaboration easier than isolation.
That’s a different game than traditional tech competition. It’s more like how the internet itself works. You build the protocols, you open-source the foundations, and then you benefit from everything that gets built on top.
Agentic Workflows and the Next Phase
What interests me most about Google’s vision is where they’re pointing next. They’re talking about agentic workflows, about scientists encoding their knowledge into specialized skills, about methodologies becoming accessible, scalable tools.
This suggests they’re thinking about a future where the bottleneck isn’t raw computing power or even access to data. It’s the ability to translate domain expertise into reusable, shareable systems. Imagine a workflow where a researcher in India can access a specialized AI agent trained on decades of medical research, customize it for their local context, and deploy it. The traditional model would require building that expertise from scratch.
The philosophical shift here is that open-source software and open datasets aren’t afterthoughts or PR moves. They’re the essential foundation. Without them, agentic workflows don’t happen. You can’t encode knowledge into accessible tools without starting from shared foundations.
Building Communities, Not Just Tools
What strikes me about the communities of practice they’re building in India, Korea, Japan, and Australia is the intentionality. They’re not just open-sourcing technology and hoping people use it. They’re actively building communities of developers around these tools. They’re creating the social infrastructure that enables adoption and iteration.
This is different from previous generations of research infrastructure. It recognizes that tools alone don’t drive adoption. People do. Communities do. Creating spaces where local developers can learn, share ideas, and solve local problems with these tools is what actually accelerates progress.
I keep coming back to this idea that the measure of their open-science philosophy isn’t the number of papers published or the elegance of the algorithms. It’s the real-world impact achieved by their partners and end users. Nonprofit organizations solving energy problems. Medical institutes improving triage workflows. Conservation projects identifying species from camera trap footage using SpeciesNet.
These aren’t edge cases or proof-of-concepts. These are the actual impact surface of the entire research infrastructure.
The Acceleration is Real
Here’s what I think matters most for developers right now. The model of innovation that Google Research is demonstrating actually works, and the evidence is overwhelming. Open science and collaborative development don’t reduce innovation velocity. They increase it. Multiple teams iterating in parallel on shared foundations produces better results faster than isolated teams developing everything from scratch.
This has immediate implications for how we should be thinking about building tools and platforms. If you’re creating something useful, consider what open infrastructure you could contribute. Not because it’s nice, but because the ecosystems that form around shared infrastructure generate value that isolated tools never reach.
The transition into what Google is calling “AI-enabled science” will probably be defined by which institutions and projects mastered the art of collaborative infrastructure. The breakthroughs we’ll celebrate in five years likely won’t come from the teams with the biggest budgets or the most proprietary data. They’ll come from the teams that figured out how to leverage the most collaborative ecosystems effectively.
That’s not idealism. That’s just how complex problem-solving actually scales.