Three of Seattle’s top scientific institutions are launching a nearly $95 million research initiative that will generate data and train AI models to design proteins and genes that don’t exist in nature — sharing the results freely to help others develop new medicines and materials.

The initiative, called AI BioDesign, brings together the Allen Institute, the University of Washington and Fred Hutch Cancer Center, with funding from the Fund for Science and Technology (FFST), created by the estate of Microsoft co-founder Paul Allen.

AI BioDesign is led by David Baker, the UW biochemist who won the 2024 Nobel Prize in Chemistry for using computers to design new proteins, and Jay Shendure, a leading genome scientist at the UW and the Allen Institute.

The plan is to “hijack a lot of the machinery that evolution provided us” — the cellular assembly line that turns DNA into proteins — to design and measure millions of novel biological molecules, Shendure said in an interview in advance of the announcement.

That will help AI models learn the rules of biological design from a huge set of examples, instead of inferring them from the relatively limited number that nature has produced.

The field, Shendure said, is “putting too much emphasis on taking the cranks that we have and just running with them, as opposed to building the right cranks.”

The goal is to make designing biology more like ordering a part: a molecule that latches onto a cancer cell, for example, or a genetic switch that fires only inside brain cells and nowhere else.

Potential outcomes could include everything from new therapies for disease, to proteins that dissolve plastic in the environment, to cells that travel through the body in a programmed way, said Sanjay Srivatsan, a Fred Hutch assistant professor who leads the cancer center’s work on the initiative, in a video released with the announcement.

“For the first time, the speed of AI is beginning to match the experimental power of synthetic biology,” Baker said in a statement. “That changes the question from ‘what has nature already made?’ to ‘what else is possible, and how can we test it?'”

Where the money goes

The Fund for Science and Technology is providing $94.6 million for AI BioDesign over five years. The foundation launched publicly last year with a mandate to direct a large share of Allen’s fortune into bioscience, environmental and AI research.

The funding from FFST is allocated as $46.1 million to the Allen Institute, $43.8 million to the UW and $4.7 million to Fred Hutch, according to an Allen Institute spokesperson.

The initiative had 62 people as of mid-August, including some new hires and others redirected from existing projects at the three institutions. The UW accounts for 41 of them, the Allen Institute 13, and Fred Hutch eight. AI BioDesign is expected to continue growing over time.

“AI BioDesign is exactly the kind of ambitious, collaborative science FFST was created to support,” said Marc Malandro, the foundation’s chief programs officer and co-lead, in a statement. He joined FFST in May after nearly a decade at the Chan Zuckerberg Initiative, most recently as chief operating officer of CZI and the Chan Zuckerberg Biohub Network.

Malandro and Chief Financial and Operations Officer Liz Carey have been leading FFST on an interim basis since founding CEO Lynda Stuart stepped down in May.

Inside the lab

On a recent tour of the AI BioDesign lab, research associate Jack Boylan pulled up results from a run he’d done on their new DNA sequencer that morning — on free kits donated by a neighboring biotech company, a year past their expiration date.

“We decided, let’s give it a roll,” he said. It worked fine.

The sequencer is what makes the whole approach possible. It reads all of the millions of DNA sequences in a single tube at once and reports which ones performed. One recent experiment ran 6 million distinct sequences through it at once.

“The scale comes not from robotics, but from parallelizing inside the test tube,” said Jesse Gray, executive director of strategy and platform for AI BioDesign and the Seattle Hub for Synthetic Biology, and a former Harvard Medical School geneticist.

The lab, at Dexter Yard in Seattle’s South Lake Union neighborhood, a short walk from the Allen Institute’s headquarters, is organized into teams of five or six people, each working on a different design problem.

A separate four-person team of machine-learning specialists takes the incoming results and works with the bench teams to decide which experiments come next — the ones that will teach the models the most. Each round is judged on how much the models improved.

The Allen Institute calls projects like this “accelerators,” a term Rui Costa, the institute’s president and CEO, traced back to Paul Allen himself. The word came up in early planning sessions, Costa said. Allen wanted to “exponentially accelerate the field.”

Other accelerators at Dexter Yard include the Seattle Hub for Synthetic Biology, the Allen Institute’s collaboration with the Chan Zuckerberg Initiative and the UW, which Shendure also leads; and Cell Science, which works on engineering cells to assemble themselves into tissues.

The Allen Institute for AI (Ai2), the separate Seattle research organization also founded by Paul Allen, is involved informally rather than as a funded partner, Costa said.

Its robotics team has been talking with AI BioDesign about scaling up the protein work, and the two expect to collaborate on models and on tools that generate research hypotheses.

Why give it away

The decision to focus on open science also came from Allen, Costa said in an interview this week. “He was so visionary in the early 2000s: radically open science to exponentially impact and change fields, not to compete.”

That raises a question the initiative will face as soon as it produces anything valuable: what happens if a company builds a lucrative drug on data given away free? In traditional science, Costa said, being beaten to a discovery counts as a loss. Here it’s the goal.

“We would be so lucky if many companies would be taking this data and changing the world for good,” he said.

At the same time, Costa left open the possibility of the three principal institutions spinning out their own startups, nonprofits, or other initiatives from the work done by AI BioDesign.

Betting against the field

AI BioDesign’s approach runs against much of the current thinking in the field. Costa said most efforts to apply AI to biology are chasing a single general model that could answer questions about how any cell works. AI BioDesign is betting on the opposite: narrow models built for specific design problems, trained on data generated for that purpose.

“This project is a clear bet on a different way of doing things,” Costa said.

The people running the initiative are careful not to oversell. Gray said it remains an open question as to whether their approach beats the alternatives. “The jury’s still out,” he said.

Shendure put it plainly: “It’s never as easy as you think it’s going to be,” he said.

Costa said AI BioDesign needs to show real progress within 18 to 24 months — ideally even sooner — and expand to researchers around the world within five years.

GeekWire is profiling over the next few weeks some of the people and teams that are shaping the evolution of Microsoft in what we’re calling its “Microsoft 2.5” era.

From AI Frontier Lab to Frontier Ecosystem: Microsoft got a foothold in AI thanks largely to its partnership with OpenAI. But that’s not the way it is planning to continue growing its AI business.

Inside Microsoft AI (MAI), the Microsoft Superintelligence team is focused almost entirely on building its own frontier-level models. That team already has developed a handful of home-grown offerings, including MAI-Code-Flash for writing code faster; MAI-Cyber-Flash, a cybersecurity model; and MAI-Image, a model for creating images.

The head of the Superintelligence team is Ali Farhadi, corporate vice president of AI. Farhadi, who joined Microsoft five months ago, is also a professor at the University of Washington, where he has worked for nearly 15 years. He was previously CEO of the Allen Institute for AI (Ai2) and before that was an AI and machine learning leader at Apple for more than three years, after it acquired his startup, Xnor.ai.

When he joined Microsoft, Farhadi said in a LinkedIn post that he believed “Microsoft has all the pieces to win in this AI race: data, search, coding, infrastructure, agents, software and the world’s biggest Fortune 500 companies taking dependencies on Microsoft every day.”

Farhadi elaborated on that in an interview with GeekWire this week. AI is shifting from a “Frontier Lab” era to a “Frontier Ecosystem” era, he said. It’s no longer just about training models; it’s about integrating the models with enterprise data, platforms, distribution systems and customers in a trusted way.

The next battlegrounds in AI will be around cost, reliability, specialization, and deployment at scale, rather than simply building larger models that beat others in benchmark scores, he said.

“If you look around, there are not that many places to have all these missing pieces together at scale, especially if you add the element of trust to it,” Farhadi said.

Cutting through the AI noise: Farhadi said his management philosophy is grounded in the importance of personal relationships, which are especially key in big organizations. People need to understand your rationale and to trust you can deliver on what you’re tasked to do, he said — an approach that has served him inside both Microsoft and Apple.

Staying on top of the flow of information while filtering out the AI noise makes prioritizing crucial. The team has “a long list of things that we believe we should be doing,” he said, but much of it stays on the back burner to maintain a “laser focus on delivering on the main mission.”

The priority is building high-quality models, both generalist and domain-specific. On the domain-specific front, Microsoft is working with the Mayo Clinic on a healthcare-specific model based on Mayo’s own clinical data, as well as Microsoft’s cybersecurity and coding models.

The thinking: For a lot of enterprise work, a narrower model beats a bigger one.

“If you can do something at [the same] quality or better quality at a fraction of a cost, it’s just a no-brainer. And having a way to specialize to domains, to industries, to enterprises is one way,” he said.

Microsoft execs have referred to this approach as a “hill-climbing machine,” meaning the ability of a model to scale and continuously improve within a specific domain. Microsoft is coupling the hill-climbing with “frontier tuning,” like it is doing with the Mayo Clinic. Frontier tuning includes customizing frontier models; keeping proprietary data private, preserving institutional know-how; and avoiding leaking intellectual property (IP) into shared models.

“We all thought that IP is your data,” Farhadi said. “But we learned that IP is also how you work.” And that’s why safeguarding these elements is so crucial.

Open all the things? Farhadi led an expansion of open-source AI development at Ai2, the Seattle-based institute founded in 2014 by the late Microsoft co-founder Paul Allen. While Microsoft has contributed to the open-source community on various fronts, including AI tooling, it hasn’t open-sourced its frontier models.

Farhadi said he personally remains “a big advocate of open source,” but noted that the industry has changed since his Ai2 days as there are now more credible Western open-source models and businesses forming around them.

He didn’t rule out Microsoft doing something in open-source models, or the somewhat less-open “open weights” area, but there’s seemingly nothing happening on that front in the near term.

In the coming months and beyond, the focus of Farhadi’s team is helping Microsoft turn into a Frontier Ecosystem by building cutting-edge AI capabilities; helping enterprises create their own tuned versions of them; continuously improving models; and making sure customers keep control of their own destinies and data.

Success for Microsoft’s Superintelligence team has nothing to do with the idea of Artificial General Intelligence (AGI) which OpenAI, Anthropic and others have positioned as their ultimate goal over the years. In fact, when I asked Farhadi about AGI, he said, “I don’t understand what that means.”

Don’t worry, Ali. You’re not the only one.

How interconnected is Washington’s tech industry? Enough that a large share of the state’s companies can trace their lineage to Microsoft, the University of Washington, Amazon, and a handful of other institutions.

A new visualization from the Washington Technology Industry Association (WTIA), unveiled this week, charts those family trees. But the “Washington Tech Universe” map offers only a partial view: Washington is home to 25,000 tech companies, and just 625 are featured.

“This is not a ranker of all the best companies,” said Nick Ellingson, WTIA’s vice president of innovation and entrepreneurship, during a presentation at Seattle Tech Week. The point, he said, is to show the region’s connectivity and to make the case for investing in the community as a whole.

The “Tech Universe Map” comes 11 years after the trade group published a similar visualization. According to Ellingson, the update came because people kept asking for it, not because of a single event. It’s unrelated to WTIA’s efforts to help Washington establish a public AI narrative.

That said, the 2026 edition is markedly different from the one in 2015: The new map looks at the entire state, aiming to comprehensively chart the connections among different companies, while the prior version was limited to companies in Seattle, with a more narrow focus on acquisitions and similar data.

Microsoft and UW produce the most founders

WTIA’s data shows that today, Washington has four main founder “hubs,” with Microsoft being the largest. About a quarter of the mapped companies — 161 of 625 — have at least one founder who came out of Microsoft. UW is the second with 143 companies, followed by Amazon, which anchors 58 firms. Google rounds out the group with 20 connections, though it’s a pipeline that didn’t exist in WTIA’s 2015 map.

UW isn’t the only school producing founders. WTIA’s map traces company lineages to Washington State, Western Washington, Central Washington, Eastern Washington, Whitman College, Seattle University, and Seattle Pacific. Still, UW accounts for 70% of the map’s university connections.

Broken down, the data shows that nearly two out of three companies (64%) grew out of another company listed on WTIA’s map. A third came out of a university, or out of a company that operates in Washington without being headquartered here.

Google is the clearest example of the latter since it’s based in California but has a significant presence here. Moreover, WTIA found that 44% of mapped companies had founders who previously worked at two or more Washington organizations before starting theirs.

Ellingson called the hubs “gravity wells that bend the entire region toward the next generation of founders” in the announcement.

However, he cautioned that this pipeline concentrated around four main sources could be a risk. Some of the hubs he expects to grow next, such as Google, OpenAI, Anthropic, and Nvidia, are headquartered elsewhere and maintain engineering centers here, a presence that is easier to scale back. Microsoft, Amazon, and UW aren’t going anywhere. The next generation of hubs has no such guarantee.

To mitigate this risk, Ellingson called for broad community support for these hubs, saying it would keep the flywheel going.

These companies, he said, “are growing not just the jobs at their companies, but they’re creating the next employers, venture-scale startups, and tech companies that go on to build amazing things, and hire the next generation of talent here and bring more talent to the area, who then go and create their own startups.”

The next hubs are forming around AI

WTIA also recognized AI’s impact on Washington’s tech ecosystem. Although the technology was not a formal selection criterion, it became evident that AI would be a dominant theme among the featured companies. In fact, firms like Read AI, Karat, Yoodli, Outreach, and Pictory appear on the map for the first time. And Ellingson revealed that “many of the startups on the map are AI startups.”

“Twenty-three percent of the AI talent in the United States is here in Seattle,” he said.

That, along with the burgeoning startup ecosystem, is why organizations like the Allen Institute for AI (Ai2) and AI House, are poised to have large constellations of their own. AI House was formerly the AI2 Incubator. It spun out as an independent entity in 2022 and rebranded in June.

WTIA noted that Ai2 is the first research lab on its map to operate as a “founder factory.”

Other AI companies making their presence known on the “Tech Universe Map” include OpenAI and Anthropic. While not Washington-native, both AI model makers have established or expanded their outposts in the state since 2015.

Ellingson predicted that, like Ai2, both would eventually become major hubs.

How companies were selected

To create the “Tech Universe Map,” WTIA started out with a dataset of 3,500 Washington-based tech companies with at least $1 million in funding or revenue according to PitchBook.

The group was filtered further to those that were headquartered or had notable engineering centers in the state, were still active, and had “meaningful” Washington-grown connections through founder or university lineage. The GeekWire 200 was also used in the process.

WTIA enlisted the help of UW graduate Jessica Forcucci and a team of designers to create the visualization. In brief remarks, Forcucci explained her vision for the “Tech Universe Map,” saying the goal was to “demonstrate the interconnectivity” these companies had with each other.

Predicting what the next map will look like

As Washington’s tech ecosystem evolves, Ellingson predicted there will not only be bigger constellations of AI companies, but also quantum, fusion and advanced energy, and space and defense. He believed more tech clusters will blossom statewide beyond King County.

Ellingson said WTIA is seeing real growth in Wenatchee, the Tri-Cities and Spokane. Those regional clusters are small now, he said, but he expects them to be substantial by 2031.

To make the next map happen, Ellingson urged people to open doors for others, make introductions without expecting anything in return, and give first, building community and forming new constellations.

Posters of the Tech Universe Map are available for purchase.

Note: GeekWire is a media sponsor of the Tech Universe Map project.