24 Dec 2025 · 27m · Rana el Kaliouby, Bob Safian, Anima Anandkumar, Ravi, Raquel Urtasun, Andy Hawks · 17 quoted facts
Industry now produces most frontier AI models, but the panel argues this is a partnership, not a takeover. Anima Anandkumar, Daphne Koller and Raquel Urtasun say AI that models the physical world needs hardware, data and cross-discipline teams that universities struggle to fund. They call for porous walls between academia and companies, plus open-source models and shared public datasets.
The central tension is between scale and openness. Industry has the money, compute and engineering for physical AI such as weather models, autonomous trucks and drug discovery. Academia has breadth, freedom and talent. The panelists worry that the funding gap, compute shortages and a few huge bets could leave public research behind, so they push open models, open data and more efficient AI.
Daphne Koller says building AI that touches the physical world takes years of cross-disciplinary work, custom hardware and custom data. Academic incentives and infrastructure rarely support that, so those breakthroughs are likely to come from companies.
Raquel Urtasun says Waabi trains its self-driving system mostly in a simulator built to be as realistic as the real world. That lets it face dangerous and rare situations without consequences, the opposite of the industry habit of racking up road miles first.
Anima Anandkumar argues discovery is rarely held back by a lack of ideas. Physical experiments and instruments are slow and expensive, so AI that simulates the physical world could cut R&D costs far more than language models raise productivity.
Anandkumar says the AI weather model she helped build at Nvidia is tens of thousands of times faster than traditional forecasting and runs on a desktop GPU. Releasing it under an Apache license let startups and countries in the global south adapt it with their own data.
Daphne Koller points to the UK Biobank, funded by the UK government and the Wellcome Trust and open to researchers at modest cost, as a model. She says such datasets are a high-return investment because they let many researchers publish quality work.
Not entirely, according to the panel. Rana El Kaliouby cited figures that 90% of frontier models come from industry, but Daphne Koller said academia still matters and Anima Anandkumar called it a collaboration, not a competition.
Physical AI is AI that understands and simulates the real world, such as weather, materials, vehicles or human biology. Anima Anandkumar says it could sharply cut the cost of R&D because far fewer slow, expensive real-world experiments would be needed.
Raquel Urtasun says Waabi uses a single neural network, meaning one AI model that learns to drive end to end, and trains it mostly in a highly realistic simulator. She says the company also checks that driving in simulation matches driving in the real world.
Daphne Koller says over 90% of drugs that enter the clinic fail in trials. Insitro builds a data factory of lab-grown cells exposed to genetic changes and other exposures, then uses AI to predict a treatment's effects before human trials.
They say open models and open datasets let startups, researchers and other countries build on top of existing work instead of starting over. Anima Anandkumar also notes that open-source AI now needs large compute, and that China is a big competitor in open models.
Stated by the speakers. Each quote matches the transcript, which shows what was said, not that it is true.
Rana El Kaliouby cited statistics that 90% of frontier AI models are released by industry and 70% of new PhDs choose industry careers over academia.
“90% of frontier AI models are now released by industry, and 70% of new PhDs choose a career in industry over academia.”
Anima Anandkumar was previously principal scientist at Amazon Web Services and senior director of AI research at NVIDIA.
“Previously, she was principal scientist at Amazon Web Services and senior director of AI research at NVIDIA.”
Daphne Koller said the transformer model originated in industrial research, as an example of industry's role in recent AI inventions.
“going back to the transformers model, it's all really originated in industrial research”
Anima Anandkumar said her group built the first high-resolution AI-based weather model in collaboration with Nvidia, Caltech and Berkeley Lab.
“We built the first high-resolution AI-based weather model, but that was in deep collaboration with my team at Nvidia, Caltech, uh, Berkeley Lab.”
Raquel Urtasun said that for nine years she has trained her University of Toronto PhD students inside industry, first at Uber and then at Waabi.
“for the last nine years I've been educating all my PhD students before at Uber, now at Waabi”
Rana El Kaliouby said that when her company spun out of MIT it had to cut many ties with the university because of conflict-of-interest rules.
“when we spun out of MIT, we basically had to, like, cut a lot of our ties with MIT because of conflict of interest.”
Anima Anandkumar argued that scientific discovery is usually limited by slow, expensive physical experiments rather than by a lack of ideas.
“scientific discovery is most of the time not bottlenecked by the lack of ideas.”
Raquel Urtasun said she started Waabi about four and a half years before the panel on the idea of building a simulator as realistic as the real world.
“when I started Waabi four and a half years ago, is that we can build a simulator that is as realistic, uh, as the real world.”
Raquel Urtasun said Waabi's simulation-first approach contrasts with the common autonomous-driving strategy of logging road miles first and building simulation later.
“this is like totally contrarian to what, uh, everybody was building, which is go on the road, crank miles, and then maybe later on you will build a simulation system.”
Daphne Koller said over 90% of drugs that enter clinical testing fail in clinical trials.
“where over 90% of drugs that go into the clinic end up failing in clinical trials”
Daphne Koller said competitors in physical AI have no shortcuts and must build custom hardware, data and models, which she described as a competitive moat.
“You have to build the custom hardware, you have to collect the custom data, you have to build the custom models that understand the physical world and causality.”
Daphne Koller said insitro's first drug, for the fatty liver disease MASH, would enter the clinic the year after the panel.
“Our first drug is heading into the clinic next year. Um, it's in the disease called MASH, which is a terrible fatty liver disease.”
Daphne Koller said more than 70 ALS drugs have entered clinical trials, four have been approved, and they extend lifespan by only a couple of months.
“Seventy plus drugs have gone into clinical trials, four have been approved. They extend lifespan by maybe a couple of months.”
Anima Anandkumar said the AI weather model is tens of thousands of times faster than physics-based forecasting and can run on a desktop GPU.
“it's tens of thousands of times faster. So what would take a supercomputer can be put in now a desktop GPU.”
Anima Anandkumar said Nvidia open sourced its AI weather model under a permissive Apache license.
“But we immediately open sourced it, right? So it's faster, it's accurate, you know, and because of that open sourcing in a permissive way with Apache license.”
Daphne Koller said the UK government and the Wellcome Trust funded the UK Biobank, a dataset open to researchers at modest cost.
“the UK government made and the Wellcome Trust made a very big investment in creating a data that is pretty much open to any researcher for very modest economic outlay.”
Anima Anandkumar said her team created the first genome-scale language model trained on all bacterial and viral genomes.
“We created the first genome scale language model trained on all bacterial and viral genomes”
Opens with stats that industry dominates frontier models and PhD hiring, and Daphne Koller's view that physical-world AI will mostly come from companies.
“it's all really originated in industrial research”
The panel argues rigid walls between campuses and companies limit research, and describes Toronto's model of training PhD students inside an industry company.
“embrace a more porous model”
Anima Anandkumar explains why simulating physics with AI could cut science's biggest cost, slow and expensive experiments.
“if we could put the physical world in a bottle”
Raquel Urtasun explains why training in a realistic simulator is the opposite of the standard approach to self-driving.
“you will never observe enough data”
Daphne Koller describes predicting drug effects with AI and data from cells, and says insitro's first drug enters the clinic next year.
“over 90% of drugs that go into the clinic end up failing”
Responding to an audience question, Anandkumar explains how open-sourcing an AI weather model spread its use, and why open models need compute.
“it's tens of thousands of times faster”
Koller says open datasets like the UK Biobank pay off hugely, and the panel discusses funding and efficient AI.
“thousands of flowers bloom”
Urtasun argues constraints and efficiency drive breakthroughs, then each panelist names one principle: human well-being, innovation and safety.
“I actually subscribe to the less is more”
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If you tuned into last week's episode, you know that I co-chair the Fortune Brainstorm AI Conference in San Francisco. It happens every December and it's a great way to wind up the year with big-picture conversations about the most pressing issues in AI from top leaders in the field. I get to moderate a few of those, and I'm sharing them here on Pioneers of AI. This week, my panel conversation about the shifting center of gravity in AI research.
In the early days of AI, much of the work was done in academic labs. But now that AI has proven its market value and so quickly, industry is spending crazy amounts of money on R D. So what does this mean for the role of academic research, and how should universities work with the business world to shape the future of AI? To explore this, I spoke with three AI leaders at the intersection of academia and industry. Anima Anandkumar is a professor of computing at the California Institute of Technology.
She specializes in using AI for modeling real-world events like weather patterns. Previously, she was principal scientist at Amazon Web Services and senior director of AI research at NVIDIA. Daphne Koller is the CEO and founder of insitro, an AI drug discovery and development company. She's also a longtime professor of computer science at Stanford. And Raquel Urtasun is a professor of computer science at the University of Toronto and founder and CEO of Waabi, an AI-driven autonomous trucking company.
Our conversation gets into the relative strength of industry and academia, how to bring the two worlds closer together, and the AI innovations underpinning the next scientific breakthroughs, especially AI that understands the physical world. I'm Rana El Kalioubi, and this is Pioneers of AI, a podcast taking you behind the scenes of the AI revolution. Anima, Daphne, and Raquel, welcome to the Fortune stage. So I want to dig right in. A decade ago, academia was the epicenter of AI breakthroughs, but the power has shifted dramatically.
And I want to share two statistics. 90% of frontier AI models are now released by industry, and 70% of new PhDs choose a career in industry over academia. And so, like me, you've both straddled industry and academia in very interesting ways. So I want to ask you this has industry officially become the innovation engine for AI? And is that a good thing or something we should worry about? And Daphne, I'm going to start with you.
Wow, okay. I wouldn't say it's the innovation engine in the sense that there is no room for additional academic research. But I think that, um, when we look at some of the where the biggest inventions have come from in the last few years, I mean, going back to the transformers model, it's all really originated in industrial research. And I would go one step further in the direction that I'm hoping this panel will eventually head in, which is as you look at AI that transcends the purely software environment and starts to penetrate into the more physical world, interdisciplinary work.
It's very difficult to build that within an academic environment. The incentives are just not there, the infrastructure, the many, many years of build that are necessary. And so I think when you're looking at that type of innovation, then I think that's going to come from within industry.
Anima, what do you think?
I mean, as somebody who's been in, um, AI for more than two decades, but especially over the last decade, being both in industry and academia, first at AWS, uh, while being at Caltech, and then in the media while still keeping my Caltech role. So, you know, when people ask me this, you know, I found it really seamless to be in both sides. Like, you know, we did a lot of like foundational work in AI and science at Caltech, because when I went there, it's like, was just at the computer vision, like kind of era, and not even natural language, right?
And here we are asking some of the hardest challenges in, uh, broader sciences, like scientific discovery. And so, you know, knowing about those challenges, it was really academia that seeded those problems that we see really take off, including weather and climate modeling. We built the first high-resolution AI-based weather model, but that was in deep collaboration with my team at Nvidia, Caltech, uh, Berkeley Lab. And so AI for science is really about this, um, deep collaborations and interdisciplinary work, right?
And I would say, maybe not in every university, but Caltech being small and interdisciplinary, I see no barriers, and that's really helped us go in a way discover all the hardest challenges. I'd like to say from quantum realm to cosmological realm, and we've seen AI make a deep impact from discovering new materials, better control of quantum devices to simulating the black hole, understanding plasma and nuclear fusion. And so this breadth of like knowledge is in academia, but of course, the engineering and the scale is in companies.
And so I continue my partnership with Nvidia and continue collaborations there. And so it's really my goal is to bring the two together, right? And also being like, uh, an advisor to companies like SK Hynix, guiding them where is the next big thing for memory. And we'll come to physical AI. But I think this is where it's not a one or the other, it's not a competition. It's really a deep collaboration and synergy.
Yeah, Raquel, what do you think? What does industry enable in AI that academia can't, and vice versa? I feel like you have found a really productive model of combining the two.
Yeah, and I would say that I'm fortunate to be an academic, have spent time in big tech, at Uber, and then being a founder over the last almost five years. And, you know, one of the things I learned over there is that we need to reinvent the model of how academia works. Because, uh, and Daphne was talking about this, if you work on physical AI, it's simply impossible to understand what are the problems that haven't been solved yet in industry.
If you are simply in an academic lab with just a few students and some resources, some small resources. So for me, really, the collaboration of industry and academia is that next model of education. And I, uh, you know, one of the things that maybe is less known is that, uh, University of Toronto has been really pioneering, uh, on this new model where for the last nine years I've been educating all my PhD students before at Uber, now at Waabi, uh, where they really get to learn what it is to do cutting-edge research, uh, without losing their freedom in order to really innovation and learning through their degree.
And then you end up with the best of both worlds, right? You understand industry, you can be a professor if you desire to, and then you get to really work on the things that matter. So
We were talking about this backstage because at MIT, it's very strict about these lines. You know, when we spun out of MIT, we basically had to, like, cut a lot of our ties with MIT because of conflict of interest. But I'm very Stanford too.
me too, Stanford too. Yeah.
Um, but it sounds like there are new models where you can kind of assign students these really complex problems to work on for their scientific research that are rooted in real-world applications.
I think it's time for academia to rethink strongly this barrier between academic research and industrial research and embrace a more porous model. Because the kind of, um, you know, successes that we heard from Anima and from Raquel, I think should be something that all universities embrace and lean into because I think it does allow you to get the best of both worlds. Whereas these very bright lines, it's like academic research with the limited resources that academia has and the limited ability to really create a cross-disciplinary team structure and the infrastructure limitations really limit what one can do in academia.
But there's some incredibly smart people in academia that if only aimed in the right direction and given the right resources, I think could contribute hugely. And it would also enrich their own research agenda in ways that they're working on really the most important problems. And I think with a really bright lines, you're losing that opportunity.
Yeah, a lot of the conversation around AI today is focused on productivity gains, but this is going to be about or is about scientific breakthroughs. And so I'll go to you next. You're using AI in all sorts of ways, but I want to dig into one particular application, which is accelerating climate solutions. And you've invented this approach, neural optimizers, to predict, model, and solve for climate events. Can you tell us more?
Yeah, absolutely. You know, I mean, this goes back to almost a decade when I started at Caltech, right? As I mentioned, everybody across campus was very interested in using AI, but they didn't know how because they don't have a lot of data. And the problems they're tackling involves deep scientific knowledge, right? And that requires modeling the physical world. Weather and climate is one example. Understanding quantum systems, nuclear fusion, being able to design better medical devices, rockets.
So all of this requires not just knowing like textbook level math. You can write down the laws of physics, but that's not so interesting, right? It's really that ability to simulate and being able to design in the virtual realm and incorporate all of the physical constraints into our AI is a big part of it. Because, you know, what a lot of recent work with language models has been for science is okay, AI or language models can come up with new ideas.
But scientific discovery is most of the time not bottlenecked by the lack of ideas. As, you know, Daphne was saying, a lot of smart people, a lot of smart ideas, right? But why doesn't that see the light of the day? Because doing experiments, going taking observations in the physical world is so slow, so expensive, building big instruments, very expensive. And if you could overcome and reduce that, to me, the productivity gains from language models is so minuscule compared to the reduction in, uh, R and D costs that could happen if we could put the physical world in a bottle.
And that's what we are doing by physical AI, to me is full understanding of that physical world, both in space and time. So in three dimensions with time, so that's four dimensions, and being able to really get to the detailed physics so we can simulate systems like weather and climate.
Raquel, I want to go to you next because you are in the autonomous trucking space, which is notoriously challenging. But you have a different approach at Waabi to train, validate, and scale autonomy. So tell us more, and you lean on synthetic data a lot.
I guess for context, uh, there is like two core ideas that make, uh, Waabi's approach very differentiated from the rest of the industry. One is this idea that you can build an end-to-end system, so a single neural network where is, uh, you know, capable of reasoning like humans do. So that, uh, you know, it can really learn with very little data to perform complex tasks like the task of driving because you're never gonna see every single situation on the road before deploying, and you know, it can have catastrophic consequences if you are not able to handle, you know, certain situations, etc.
So that's one core piece of technology. And the second was that you will never observe enough data. And data was, you know, in the era of AI, uh, is gonna be, you know, more than half the equation. So the idea was, uh, you know, when I started Waabi four and a half years ago, is that we can build a simulator that is as realistic, uh, as the real world. And if we can do this, then suddenly we can expose the system to, uh, you know, everything that potentially can happen, including unavoidable accidents, et cetera, without consequences.
And then it can learn and be trained, you know, mostly on simulation and then perform really well from day one on public roads. And just for context, this is like totally contrarian to what, uh, everybody was building, which is go on the road, crank miles, and then maybe later on you will build a simulation system. It turned out to be, you know, a a great idea. And, uh, what we can do is also validate and verify the simulator and prove that driving on simulation is the same as driving in the real world.
So now we have, you know, all the ingredients so that you have the autonomy that can generalize, you have the simulator that can simulate everything, and then we are ready for deployment. So really exciting time
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no so we are building a platform for really creating a model that allows us to answer the fundamental question in drug discovery development which is if I make this intervention in the human what is it going to do what's it going to do at the cellular level what's it going to do at the tissue level what's it going to do at the human level in terms of the clinical outcomes so we can make that prediction in silico maybe not with 100% accuracy but with way more accuracy than the current success rates of our industry where over 90% of drugs that go into the clinic end up failing in clinical trials so we you know that's a low
bar to beat but it's been incredibly hard to beat that bar and so what we've done is we've built an incredible um as Raquel correctly said data is the core of everything in modern day AI and the data that one needs in order to really um make those types of predictions is not going to be found on the internet you're not going to find the cure to ALS by reading more papers because if people had known that they would have gotten there already and this disease has no cure And so what we've built is this incredible data factory that brings in uh the prints massive numbers of cells perturbed with genetic perturbations perturbed with uh different types of exposures so
we can really start to get at causal relationships between genotypes and phenotypes. We also bring in human data where we can start to see among experiments of nature like every one of us in this room. What is that relationship between genotype and phenotype? And really integrate that together with a generative AI enabled brain to sort of make those predictions holistically. And so that's really what we've been building for the last years. And it fits squarely similarly to Raquel in the realm of physical AI, which is a different journey than consumer AI or SAS AI, which is you have to first get from zero to one.
You have to build the basic platform that allows you to get to these capabilities before you can start to sort of prove it out and scale it. But once you do, you've created this incredible competitive moat because everybody else who wants to take that same journey, basically, there's no shortcuts. You have to build the custom hardware, you have to collect the custom data, you have to build the custom models that understand the physical world and causality.
And you have to do it all, as Raquel said, without actually killing people in the process, which is something that, you know, when bits meet atoms is a real risk. And so we've done that, and 2025 was for us a real banner year because not only did our platforms hit escape velocity to the point that every time we turn the crank, more stuff comes out, but we've actually started to see these proof points in the context of real drugs that are coming out.
Our first drug is heading into the clinic next year. Um, it's in the disease called MASH, which is a terrible fatty liver disease. The one that comes soon after that is in ALS, which is Lou Gehrig's disease. You may or may not be familiar with that disease. Basically, your lifespan from diagnosis is about three to five years, and there is no treatment. Seventy plus drugs have gone into clinical trials, four have been approved. They extend lifespan by maybe a couple of months.
So it's a horrible disease, worse than most cancers. And we feel based on the data package that we have, albeit preclinical so far, that we have found something that is truly disease modifying for this disease. Our partners at BMS agree with that assessment. And so we are really excited to potentially really take that and help people live, which is really the most aspirational goal that I think you can have is to have people live a longer, healthier life.
Do we have any questions? Yes, there's a question right there. Please share your name and organization.
Hi, my name is Ravi. I'm from Cognita, but I used to work for Lucid Motors. So I have two questions. One, this debate about uh academia and uh industry. There's a third leg of this whole innovation equation, and that's open source. So a lot of innovation that happened started with Linus and Richard Stallman and GPL and whatnot. But how do we make sure that that third leg also kind of grows so that innovation that flywheel keeps growing?
So that's a question, like, what do you think?
Okay, what is the third leg? Open source
Open source.
Okay. So who wants to who wants to answer that?
I mean, as somebody who spent time, you know, at Nvidia, where, you know, we built not only the first uh large-scale high-resolution AI-based weather model, so AI is able to replace traditional physics-based forecasting, and it's tens of thousands of times faster. So what would take a supercomputer can be put in now a desktop GPU. But we immediately open sourced it, right? So it's faster, it's accurate, you know, and because of that open sourcing in a permissive way with Apache license.
So, you know, startups build on this, and especially many countries in the global south that didn't have very dedicated weather forecasting stations could use this global model and then fine-tune on their own data. So that has just like created this whole swarm of activity in the field, but it started with that open sourcing, and it's been the same principle, a lot of robotic simulation. While I was there at NVIDIA, we started with open sourcing, and now that's taken off as well.
And I think that's a really important pillar. You know, there are some companies like NVIDIA doing that extensively. And uh, but that's, I agree. I think both in academia, but also national labs, if we can supercharge with more supercomputers, you know, like what's been announced. So we need compute too, right? It's not like the open source of the old era where the software engineers by themselves, you know, they are happy writing and putting it out to the world.
We need to be able to train on large data, open source large models. And of course, in China, that's a big competitive aspect that we need to be doing that here as well. It can't be all done in China. And I think that's been a good driver to encourage efforts like at Allen Institute and so on. So I really hope we get the resources. That's really the primary bottleneck.
Build on this, um, because compute is one aspect of it, the mo, open sourcing the models, but what about the data?
So thank you for asking because I was going to comment on exactly that. I think there are insufficient efforts out there to create and curate large amounts of high-quality data that can drive the kind of discovery that we're talking about. I will point to what to my mind is one of the highlights of this, uh, at least my industry, which is the UK Biobank, where the UK government made and the Wellcome Trust made a very big investment in creating a data that is pretty much open to any researcher for very modest economic outlay.
The US has finally opened up all of us, which was a massive multi-year effort here in the US. It's not quite as rich as the UK Biobank, but still very useful. I think other resources like that are an incredibly high ROI investment for governments and philanthropic institutions to make an investment in. Because once you open it up there and it's really large and high quality, thousands of flowers bloom. The number of papers, quality papers and insights that came out of just the UK Biobank blows my mind.
And I think more such and GTEx and um, and the cancer genome atlas, more resources like that are absolutely critical, and I wish more governments and philanthropic institutions would fund that.
I just want to add that our team also used the UK Biobank. We created the first genome scale language model trained on all bacterial and viral genomes and that whole area too, like protein design, enzyme design, you know, predicting new variants of concerns. Was tagged
P P is what gave us AlphaFold, exactly.
More questions? Yes, all the way at the back.
Hi there, Andy Hawks, Rebirth Systems, and uh, recovering physicist. Um, excellent panel. I'm really curious what this group thinks about where the future of fundamental research and development will occur and how it will be funded, particularly given the changing landscape of federal funding and the increasing concentration of VC funding in a in a few very large bets.
Cal, do you want to take that?
Yeah, sure. So maybe as a Canadian in the room, I will say that there is opportunities as well uh for other places uh to to play a role uh in in terms of fundamental research. But uh, you know, it's an interesting question in terms of, you know, we see a lot of the more is more, and I actually subscribe to the less is more, meaning that a lot of the uh, I will say breakthroughs come when you uh have spare resources that actually force you to think more.
And I think that as a whole community, we need to spend time into building sustainable AI, which is think about the use of data, the uh architecture, the learning algorithms, et cetera. So that, you know, instead of thinking of powering uh, you know, the entire New York versus powering uh, you know, your uh, you know, training your model, whether we can actually build technology that can the entire world can actually benefit, right? And I don't wanna be in a in a position where somebody has to make a call between that family gets in the winter to have electricity versus somebody else.
I think if we actually make much more effort in terms of these really efficient models, uh, we will be in a much better place. I was gonna just add that, yes, hardware is a big part of it. Good to see you, Andy. I know we've connected since the early days of Cerebras, so on the hardware equation, like, you know, really being able to innovate not only new kinds of hardware, but the efficiency aspect of it is gonna be a big part of less is more.
So I just wanted to add that.
Last question, and very quickly, just one word. What's one principle we must not compromise on as AI becomes more physical and as we kind of prioritize scientific discovery? Daphne.
Wait, why do I have to start all the time? I'm looking at all people.
You can go. One word, very quickly.
Human well-being.
Love it, Anima.
Innovation, you know, always pushing the frontier of what's possible. Raquel.
Safety, always safety.
Yes, I love it. Thank you so much. Thank you so much.
Thank you.
It felt awesome to share the stage with these three inspiring leaders. As a startup founder and investor who comes from academia myself, I relate to the ways they care about both the research and practical implementation of AI. I believe that those of us who've been in this world for decades bring unique perspectives to this inflection point for business and society. I still have so much that I'd love to explore. Sometimes I dream about going back to get another PHD.
Yes, really, I do. And yet I also adore the energy and innovation in the startup world. Luckily, I get to have a foot in both. And I see the future of AI relying on stronger partnerships between these worlds. Pioneers of AI is a Wait What original production. Our executive producer is Eve Tro. Our producer is Rachel Ishikawa. Our senior talent executive is Stephanie Stern. Mixing and mastering by Brian Pute. Video editing by Eric Purcell. Original music by Ryan Holliday.
Our head of podcasts is Lital Malad. You can join the conversation across social media platforms. Just look for us at Pioneers of AI. Thanks so much for listening.