CIENCE

See which workplaces made this year's list.

GPTW Information Technology Large 2022-CIENCE_7039882-3

Employees say: 
 
“Everybody here has a voice. Everybody is encouraged to succeed, and are given the resources and skills to do so. Everyone is celebrated here, from small victories to really big ones. There is a sense of family here, even when some of us have never met in person due to remote work. I feel connected to my team members and to the executive team. Transparency is also highly respected and valued here.”

Best Large Workplaces in Technology

The companies on this annual list make a conscious effort to be supportive, inclusive workplaces.

GPTW Women 2022-CIENCE

Employees say: 
 
“I have never worked for a company that seems to genuinely care about its employees. My child has had a string of illnesses and I was afraid I would lose my job because of the number of absences. Instead, I have had so much support and care given to me that I cannot even explain the difference it has made.”

100 Best Large Workplaces for Women

To determine millennials' workplace preferences, Fortune's research partner Great Place to Work analyzed feedback representing more than 6.1 million U.S. employees.

GPTW 2022 Millennials List Large-CIENCE

Employees say: 
 
“I have never worked for a company that seems to genuinely care about its employees. My daughter has had a string of illnesses in the last month, and I was afraid I would lose my job because of the number of absences. Instead, I have had so much support and care given to me that I cannot even explain the difference it has made.”

100 Best Large Workplaces for Millennials

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CIENCE | Fortune

CIENCE Company Profile, Stock Price, News, Rankings | Fortune

Known to many as "On Running", On shoes are everywhere: Olympic podiums to Paris Fashion Week to corporate offices. 

Today, the Swiss company offers premium head-to-toe sportswear designed by teams of athletes and scientists. Yet as On’s product mix expands, the company risks losing focus on the elite running shoes that first won customers. 

In this episode of Behind the Business, Fortune’s Ruth Umoh visits On’s headquarters, sports science lab, and the company’s new LightSpray factory in Zurich to learn about On’s next steps into innovative new footwear, apparel and accessories.

How On is Rewriting the Rules of Running Shoes | Behind the Business

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Every world record is falling at the moment, and it is because of the technology. Nike and Adidas have dominated performance run for decades. Then on stepped to the starting line. We were kind of afraid the shoe would fall apart and she ended up winning Boston. Its sneakers appear on marathon podiums. They're also shown up in airport terminals, art fairs, and even fashion weeks. The company wants to become far more than a running shoe brand. Your identity is expressed through your body. The shoes tells you a story. It's a feeling. But as On expands into apparel and accessories, it risks losing focus on the high performance running shoes that put it on the map. The question is, can this underdog become the next great sportswear company without losing the performance DNA that made runners believe in it? The biggest risk is not to take the risk, and we said, hey, let's go all in. What's there to lose? I'm here in Zurich, Switzerland, and I'm here for a very specific reason, that is to visit On Labs, which is this building right behind me. On started as a premium shoe brand, but it has since expanded over the last 5 years into apparel and clothing, which means you're just as likely to see the brand on a running trail as you are in a museum, in an art gallery, or even a fashion show. Swiss triathlete and Ironman champion, Olivier Bernard founded on in 2010. Looking for a better running shoe, he built an early prototype by attaching pieces of a rubber hose to the soles of his sneakers. His shoe concept didn't convince investors. So Bernard, along with friends David Aleman and Casper Copetti, decided to build on themselves. Today, that garage style experiment has evolved to challenge some of the biggest names in sportswear. On has surpassed 3. billion Swiss francs at this point in annual sales. It continues to grow rapidly. What do you think is the biggest driver of the company's growth? I think the biggest driver is the product. It's the innovation. It's that field that's running on clouds, and it's not just in footwear, but now we have brought that to apparel as well, so we're really growing toe to head. This jacket was created with one of our trail athletes. Now these trail athletes are running oftentimes up to 170 kilometers with 10,000 m of elevation. Their kit has to be so precise because it is life or death. On built its reputation around running. Even today, consumers often refer to the company as On R or college shoes on clouds, but On has much bigger ambitions, namely in clothing and accessories. Its apparel business grew more than 60% last year, helped by collaborations with Zendaya and luxury fashion house Loeve. But as On broadens its reach, it has had to avoid losing the product focus that made the brand successful in the first place. It's a balancing act that has challenged even industry leaders. Is there a fear that you'd dilute the brand if you move away from being hyperfocused on the athlete? We will always remain hyperfocused on the athlete. There's room for both. It's never an or, it's an and. But now we have the runway to build this into a larger collection where we can express that performance and technical ability into these more expressive everyday silhouettes. These are not opposites, and what makes technology performance for a runner translates into comfort for everyday use. Since its 2021 IPO, A's net sales have surged nearly five times over, reaching $3.8 billion in 2025. But the company is still a fraction the size of Nike and Adidas. A's strategy is to outinnovate them, investing heavily in research to develop premium products for serious runners. On registered last year 200 patents, so it's really a drive to reinvent how we're doing footwear and how we are reshaping the technology, including manufacturing. When I think about on, I think about sports. I think about elite athletic performance. However, when I look at your growth, it's not just athletes, it's also the everyday consumer. It is nurses who want comfortable shoes when they're working. On a hospital floor, how do you navigate the two? I feel what happened over the last 20 years, 15 years is that sports really moved at the center of society, and so it has completely changed how we dress, how we navigate through our weeks and lives. We call it the movement class, and so we have the opportunity to take this secular shift and build the most premium sports brand. That's our goal in that opportunity. Welcome to our mega space. How do you go from an idea, a concept to something that's a patentable product? It always starts with a Y. We are not creating technologies for the sake of creating cool technologies. We are starting with a question we want to answer, and then we work in an interdisciplinary team. So an innovation team has sports scientists, has sports engineers, has material scientists, chemists, and polymer scientists. This is an example of light spray. So this is the light spray Club Monster 3 that just came out. Very light, very, very light. We pretty much reinvented how you do a shoe upper by having a robotic arm that spins a 1.5 kilometer filament into a shoe in 3 minutes. So what you're seeing here in Zurich is the first light spray factory. Here you see a loud plume strike light spray that's just starting the process where the robot moves the last so that this filament will go around. It takes about 3 minutes for a whole shoe to be sprayed. Compare that to a manufacturing line where you would have 20 to 300 workers going through each step, and literally I mean you can take the shoe from the robot, put it on, and go on a run. The next generation will not want to work in a footwear manufacturing side, so automation is a must. And now we are scaling these factories and we want to bring them closer to where our consumers are. That will allow us to react to trends. Right now we have a lead time of about 4 months, to maybe get that down to 2 weeks. I would imagine that you're investing heavily then in R&D. I would imagine that it's cutting at least to an extent into the operating budget. Where are you actually seeing ROI at this point with Light Spray in particular? Innovation is creating a value that consumers feel and are willing to spend on that gives us the high gross profit margin that allows us to invest more in innovation, and so it's a flywheel. We're not necessarily looking at the ROI on a per project basis, but we look at it for a very long term. This is the engine that drives us forward. Are you testing the shoe out on a runner's foot? Do you bring them to a lab and have them run and track the data and the analytics and their sled and Upstairs we have an athlete lab where we put people on the treadmill. We have about 20 high speed cameras. We have horse plates. And they wear a mask and through their breathing we can measure at a certain speed, certain heart rate, how much energy they're using. A operates a dedicated sports science and athlete testing lab located a short walk from the main office building. Equipped with high-tech treadmills, VO2 masks, and motion capture cameras, resident athletes work with researchers to benchmark shoe performance against An's own products and lead-in competitors. The 3D animation really shows the live motion of her left leg. Because we have sensors on her foot, so we can analyze the breathes, breath in, breath out, we see the heart rate, we see the VO2, so how much oxygen is used, and all of this gives us an indication towards how much energy is she's spending to keep this running speed. And what we want is we give her another shoe, she can run the same speed, but she loses less energy. Well, maybe we need to crank it up. Beyond the celebrity collaborations and cutting edge robotics, Aleman and Copeti say the company's mission is rooted in something much simpler, creating products that help more people move. Our mission is to make people move more and more freely, and so innovation and the performance technology is the means to that. We believe if people move they are more creative, they are happier. Basically movement ignites the human spirit. And so if we can just get people to feel less pain in running or feel better in yoga class or even just feel better on a stroll through Central Park, I think we have achieved that mission.

When it comes to drug discovery, most AI applications stop at prediction. A far more difficult problem is engineering the actual therapies—designing candidate molecules as well as the models to evaluate them. Lila Sciences and Nvidia are working together to build the "scientific superintelligence" to close that gap—with hope that it can accelerate the pace of discovery and speed drugs to market.

Geoffrey von Maltzahn, Chief Executive Officer, Lila Sciences 
Kimberly Powell, Vice President, Healthcare, NVIDIA 
Andrew Nusca, Editorial Director, Brainstorm, Fortune 

A More Potent Pipeline

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Um, I'm excited to have this conversation, so I'll kick it off by just putting it right in front of you. You both believe that science in this country is underinvested. Why? Oh, OK, I'll, I'll give a perspective here. One, there was a significant reduction by our current administration across the board of science. You heard what happened to funding in NIH, um, tens of billions of dollars eliminated at the exact moment where biology and healthcare is hitting its chat GPT moment. OK, if you think about um the the scientific method, if you actually say it out loud, what is it? You, you pose a question, you go gather context, you make observations, you reason over it, and then you take some action and that action provides more information and more context. What have I just described? Agents. They are exactly parallel, and so the scientific method is entering this new paradigm right now as we speak. And so we need to empower every scientist to have this tool and have it in abundance. And what you're going to hear a lot today is it's not only about um these scientific agents maybe being able to read biology or read science, it's actually can it do science. Agents is all about being able to do work. And so while science for the last many centuries has been human limited. We are now able to marry up both agentic science and labs and automation, physical AI together to create this closed loop on a massive accelerated scale. And so if we defund now while the rest of the world leans in, which Europe is leaning in, which a lot of the Asian countries are leading in, we will be left behind and we just cannot. Jeff, I'll, I'll pull on that final thread in that a lot of the reactions to changes in investments in science are based on the paradigms of human science in the past. And you know, expressions that it's unfortunate that we're, you know, underinvesting relative to what we've done historically. What Kimberly just landed is that the even bigger reason to be investing aggressively right now is that for the first time in human history, a dial called scientific intelligence. is in the picture and for the same reason that there were scaling laws of stuffing the internet into larger models with more compute that created higher intelligent AI systems, one can now conceptualize that scientific method that Kimberly was describing as a means by which to open up a new scaling law of scientific intelligence. And since scientific intelligence is the most important determinant of ROI in science, Um, it is going to become, it should be a national priority for the US and our allies to build the highest scientific intelligence on Earth. Falling below the scientific intelligence of one's adversary at a corporate level, at a sovereign level, is almost like an unimaginable competitive disadvantage. Yeah, I should ask the room how many, how many folks in here would call themselves scientists? Raise your hand. Alright. So for those who didn't raise their hands, you know, you've talked about in our conversations prior to this, um, science, um. Technology and AI in particular being a great unlock for science. Science is limited, is, is, is kind of how it has been characterized before. For those of us who are not scientists, what's so limited about science today that AI would be a great unlock. Uh, I'll take a stab at it, but, but Lila's building it. Uh, I'll take it from our perspective from being a technology company, um, as I described, what is the work that is now enabled by agents. Um, if you think about the scientists, where they start, they start in their own human observation, so that's their own human experience. So you're already right there limited by your experience and what you've observed in the world. So the next thing you have to do is you have to augment your own human experience with more context. And that means reading, let's take life and let's lean into life sciences to just put science in perspective. One of the most important areas of science is life sciences. And so we need to understand biology. Biology is something that the world doesn't completely understand now, that's why we still have so much unmet human disease. And so we have to now keep up, imagine a human trying to keep up with the number of papers that will be published during this talk, which is of the tens, and try to stay up on biomedical knowledge and all of that deep understanding. Um, that's again, human limited. And then they have to start asking, you know, the questions, the prompts. What is the scientific goal I'm trying to achieve? And so on and so on. And so it starts from the very first step of doing science that is human limited. And you take that all the way from what you can do sort of outside of the lab. The other really important paradigm to think about here is what we've experienced with agents and coding is incredible. You know, you're seeing 3 times the productivity of a software engineer, and that is wonderful. However, science is harder than that. Why? Because we don't have what software engineering has, which is all of this ability to verify the results. Did the code compile, did it pass the test? Did it do what you expected it to do? That's a rather simple way to evaluate and verify that your agent did what you expected it to do. When you're dealing with science, the only verifier is the lab and is the biology. Did it do what you expected it to do? And so that too has human limitation running the labs 24 hours a day, sometimes doing very exquisite laboratory work that is so, um, you know, deeply specialized, right? There's only so many scientists um that can actually go in and manufacture and work in labs. Let's say organoids and cell therapies and otherwise, right? We have other labs that do high content, um, you know, development of data, but there's a, there's all sorts. And so there's just limitations all along the way and this is what, what we are now in the moment to unlock a lot of that human limitation and connect the two together. In such a material way for the speed of science and so I, I love your perspective and maybe you should teach them about what you're building because it's incredible. Tell everybody what Lila Sciences does and while you're on the subject, what exactly is scientific superintelligence, which is a term that your company likes to use. So Lila's mission is scientific superintelligence to solve humankind's greatest challenges. That is of course a very lofty one, yeah, OK, uh-huh, um, and scientific superintelligence sounds like it's a distant part of the future, and, and of course it is. It's also an accurate statement of the past now. So many of you will have heard of Alpha Fold, a Nobel Prize winning contribution to science from DeepMind. That is super intelligent on the task of predicting how genes across the kingdoms of life fold into the engines of life that we call proteins. And um our founding team of Lila had been pioneers of applying generative AI to disparate domains of science for 3 to 6 years before Chachi PT emerged, and we'd concluded that the models we'd created had become super intelligent for antibody design, for peptide design, for designing organic molecules and even inorganic materials. And since the only thing that distinguishes human science from the rest of the animal kingdom, science, and $10 trillion of annual revenue from approximately 0 is intelligence, if intelligence goes up anywhere in science, that's the most important thing in the history of science. And as Kimberly was just saying, like, when you spend your life specializing in science for decades, you can lose track of what a flaw it is that as a species we've had to shatter the world into thousands of little subspecialties in order to be able to comprehend it. And um A wonderful piece from 2019 by last year's Turing Award winner, which is like the Nobel Prize in Computing, called The Bitter Lesson of AI, which I recommend all of you look, uh, look up, is a two-page blog post that succinctly observed something that's been even more prominent in the years since and applies to science as well. Which is that in 70 years of AI research leading up to 2019, every single time AI researchers took an approach that was specialized to the human way of doing things, Those were destroyed by the most general and scalable approach. So we concluded that the most general version of AI systems being able to run the scientific method that Kimberly described and thereby enable a new kind of manufacturing process for intelligence, like think vast buildings filled with scientific instruments running the scientific method around the clock across materials, chemistry and life sciences. With all those hypotheses and conclusions flowing into a single kind of scientific mind. would be the future of something like 100% of. We set that in motion 3 years ago and have been honestly startled by how quickly we've gained conviction that what I just described is true. It's moving very quickly. Can For the uneducated here, can AI discover a drug right now? Uh, emphatically, but it depends on how you describe the, like where the challenge starts. So everything that Lyla's been doing has been living in, um, brilliant scientists inside of Lila stating, um, that we should go after some grand challenge. I'll give you a few examples, but we've, we've been applying a common system across material science, chemistry, and life. So one of the challenges we've been after is discovering new catalysts that can split water into hydrogen and oxygen. The commercial standards today use precious metals, specifically iridium and ruthenium. Those are rare enough that I imagine some of you haven't previously heard of those elements. And um A year ago, Lila's agents were making reasonable suggestions, like one of our um uh senior team members was a professor at Caltech. This has been among his domains of his life's work. And a year ago he'd say, my my lab never tested this, but this is an interpolation between what my lab was doing and this other lab. A few months ago, he started describing that a third of the agent's suggestions didn't make any sense at all. There was no reason to believe that that element or that crystal structure had any role in that reaction. And those are now humankind's highest performing catalysts for splitting water into hydrogen and oxygen, and it's quite moving to hear him talk about it in that he feels like he's seen a scientific discovery from far off in the future. We're now seeing this across designing antibodies, designing mRNAs, designing small molecules, and I think to many people this clawed code-esque moment of when a new intelligence gets injected into science and changes it forever thereafter. It may not feel imminent, but it is right around the corner. So I can't help but think so all these things we're talking about, um, heavily heavily regulated, right? We need standards in science we we have these things drug discovery for example very long process, very expensive, very regulated um some of these other things we're talking about, same deal. If we inject AI into the mix, even patents, by the way, even scientific patents, if we inject AI into the mix, are our regulatory frameworks ready for that today? I mean, we're, there's a lot of adaptation that's gonna go on, um, but, but let's be clear on, on, you know, the subject matter of science and it being regulated for. Human consumption, I mean, I think the opportunity here is upstream of that, right? So there's, there's regulatory measures that have to be, um, clearly put in place whenever you're putting humans at risk, OK, but if we kind of put that somewhat to the side because the FDA is working really hard, you know, thousands now of, um, AI medical devices that are being approved, you know, they're, they're working really hard so that. Things that have gone through the necessary um gates are going to reach patient we you know we want these things to reach patient whether that's a new surgical robot, a new computer vision algorithm that can detect something like pancreatic cancer or otherwise, right? But if we come back to upstream of that and the ability to do science, one thing that I think is just really, really important here is um. A lot of the work that we do together is a huge investment of Nvidia's in an area we call open models, open AI, not open AI, the company, open artificial intelligence models that we do and we invest in to create a generalized somewhat capability, but in areas we can uniquely contribute. So physical AI world foundation models that Lila uses for. Their lab, um, biology foundation models that they can use to do that antibody model development, um, language, multimodal language models that can, you know, read and parse through all of the medical literature and understand that. And then they, we, we create them as open models so that amazing companies like Jeff can take them, adapt them for the core capabilities that they're trying to create. And then build in all, they're the experts, they're the experts in doing science. I don't claim to be, but I've created the conditions where we can massively accelerate their ability to build this super intelligence, and then they will build in the necessary guardrails and because the open model ecosystem exists and it's open tools and open guard railing. You create the conditions that we can create a much, you know, safer and faster and more specific level of of of intelligence for a lot of different domains. And so, you know, in that regard, we're working really hard to democratize this capability. Because if it's only closed models that exist, there's, there's not a lot of ability to engage and create the conditions that all of these applications across life sciences and materials science and all of these um amazing and important industries can thrive in the age of AI. Uh, it just wouldn't be possible. And so, so that's how we think about it. How do we create the building blocks that you can build in the necessary safety considerations and specialization so that these applications are behaving the way that they're expected to behave and put them in the hands of those subject matter experts. Now I want, I want to come to the audience in a second for a question, but I have one more for you, Jeff. Um, thank you for that, Kimberly. Um, so we've been talking about the building blocks and the foundation and just, and we've been talking about life sciences as an enormous category of things. Break it down for us. What are these new capabilities going to help us get to faster? Specifically, are we looking at solving certain types of cancers? Are we looking at certain classes of drugs that we've had difficulty cracking? What do you think is most promising? Because of these new capabilities. Yeah, what's, what's important and beautiful about this era of science is that the, the root node underneath all the root nodes that you just described is intelligence. Again, going back to the slope that is observable in the rest of the animal kingdom, science versus humans. Any extrapolation from that means that higher intelligence and science. It is going to radically improve humankind's ability to be able to do what each of us hopes a medicine will do when we step into a healthcare system, which is prevent the disease from ever happening or reverse and cure a disease that's already underway. And in fact, if you look at the last century of, of medical progress and you didn't know the order things took place, you might struggle to arrange them. In that 100 years ago humankind discovered the first antibiotics, those cure diseases every minute around the world. In close proximity to that humankind discovered vaccines. Those prevent some of the highest areas of In all of human history, morbidity and mortality from arising. Since those, the number of times we've been able to use the word cure or to prevent the category of diseases from ever happening has been um very rare. And um it's because as you wander into the labor of love that is science, it is really difficult for the human mind to conceptualize any, any fraction of the combinatorial spaces at play, like what Kimberly just described agents doing. To design an antibody or to design a small molecule is they are stepping into a combinatorial space that is atoms of the universe vast. And you know, try as we do, the human mind is not very good at navigating those sorts of terrains. And so for the same reasons that Having a model that has read everything on the internet. Is an exceptional resource for humankind, um, agents that have the ability to be able to earn the equivalent of billions or trillions of PhDs by running the scientific method around the clock and have benefited from concurrent. A cycles of the scientific method at civilization scale, right, it's going to create a kind of tool that's never existed to do what every scientist dreams of, which is sail to the very farthest edges of human knowledge and Go further. Amazing. Alright, let's go right here. Oh my goodness, we only have time for one. I will go right back here in the back, yes, with the white, your name and who you're with. Good to see you. Can you guys hear me? OK, there we go. Thanks with Nanaware from New York. Uh, I want to touch on Kimberly, your point because I totally agree with the open model need, especially when we think about identifying molecules and proteins. I totally get that. I think that's going to speed up our identification. Immensely, but when we think about actually creating the therapeutic or in my case, we create digital diagnostics and cardio metabolics. At some point we have to fix our risk management when we go to. Right? And we can do predetermined change of control, but we might be able to send updates every year, uh, once a year on post-market surveillance, for example. So how do you think about how AI, maybe this comes down to the GPU level, but I'm curious, Jeff, your thoughts as well. Traceability and design controls have to go back to the root somehow when we think about safety and efficacy, and I'm, I don't, I might be out of my depth, but I don't see how AI. How, how do we go backwards to the root when we're thinking about inputs for traceability and design controls in that regard, because then we could literally be updating FDA. On a constant basis. Yeah, I mean, briefly from a technology perspective, there's a couple ways we think about trying to tackle this problem. In, in one effort, it's how can we build, if you will, digital twin models, let's say the virtual cell, multi-scale, um, very large capable models that can represent biology and then simulate. More in in computer so that you have more, you know, efficacy, more ability to present that as simulation information to the FDA. This is, we believe is a near term future that must happen, uh, and so that's, that's one incredible thing. But I think what we can't dismiss is I'm not, we're not saying do everything in silicon. There is still a lab. There must still be a lab, but I think what you're going to be putting into the lab and that that verifier, that validation that we should speed that up much, much more quickly, narrow it down and have that one turn. I don't know that we need to trace back to what piece of information on the internet was in that model that got us there. So I think where you put the traceability. Back to should be something that was observed in the lab for the, for the, you know, and then hopefully we build this digital twin and these digital model futures that have enough fidelity, which we're just not there yet, but do eventually have enough fidelity that the FDA will also start to accept some of those measures. We must leave it there, but thank you so much, Kimberly. Jeff, thank you very much. Thank you.

Gabe Stengel studied computer science at Princeton and started his career as an investment banking analyst at Lazard. Then he did something a lot of bankers think about but never do: he quit at 23 to build the AI that would automate his old job. After 30 VC passes and two grueling years, his company Rogo just raised $160 million at a $2 billion valuation, and 35,000 bankers at firms like JPMorgan, Bank of America, and his former employer Lazard now use it. Fortune's Allie Garfinkle sits down with Gabe to talk about the future of M&A, why some Wall Street workflows will never need a human again, and what AI means for the next generation of investment bankers.

The $2B AI tool automating Wall Street | Term Sheet

Tech

Gilead Sciences moves at a pace rarely seen in pharma, delivering breakthrough COVID-19 antivirals and HIV preventatives in record time. Yet, in an industry facing a massive trust deficit, speed isn’t always enough. 

In this episode of Next to Lead, Johanna Mercier, Gilead’s Chief Commercial & Corporate Affairs Officer, joins Fortune’s Leadership Editor Ruth Umoh to discuss how the company is rewriting its narrative and balancing its patients with its shareholders. 

Big Pharma's trust gap? How Gilead is rewriting the narrative | Next to Lead

Leadership

In this episode of Next to Lead, Johanna Mercier, Gilead’s Chief Commercial & Corporate Affairs Officer, joins Fortune’s Leadership Editor Ruth Umoh to discuss how the company is rewriting its narrative and balancing its patients with its shareholders. 

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