Can Smaller Startups Compete in the AI Race?

Speaker 1 I just heard a really good joke, I have to tell it.

Speaker 1 TechPro stands for technically broken.

Speaker 1 You like that, Gary?

Speaker 2 I do.

Speaker 1 Hi, everyone, from New York Magazine and the Vox Media Podcast Network. This is On with Kara Swisher, and I'm Kara Swisher.
As we talked about on on the show last week, the AI boom hasn't slowed down.

Speaker 1 According to Pitchbook, AI and machine learning startups took home about half of the venture capital dollars in North America last year. Globally, it was about a third.

Speaker 1 It added up to more than $131 billion.

Speaker 1 So far this year, AI is still the top sector for venture funding, but a huge chunk of the VC funding in the first quarter of 2025, $40 billion, went to open AI.

Speaker 1 So the question is: is there still room for smaller, more focused startups in this AI goal rush, or is everyone just placing their bets on the big guys?

Speaker 1 My guests today are two people who have been following the story on the ground, so to speak.

Speaker 1 Gary Rivlin is a Pulitzer Prize-winning investigative reporter who has been writing about technology since the mid-1990s.

Speaker 1 He has written 11 books, including his latest, AI Valley, Microsoft Google, and the Trillion Dollar Race to Cash In on Artificial Intelligence.

Speaker 1 It follows the rise of inflection AI and why, despite deep-pocketed funding, much of the team, including the leadership, was eventually assimilated into Microsoft.

Speaker 1 My second guest, Christy Weiskell, is the senior advisor to the president of Johns Hopkins University for Innovation and Entrepreneurship.

Speaker 1 She oversees Johns Hopkins Technology Ventures, where she helps students and researchers launch their academic discoveries to the private sector through industry partnerships, technology licensing, and startup company incubation.

Speaker 1 Our live interview was recorded on April 28th at the Johns Hopkins University Bloomberg Center as part of their inaugural discovery series.

Speaker 1 It was a great conversation and says a lot about the state of the industry today and where it's going tomorrow. Have a listen.

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Speaker 1 Christy, I've just met you, but Gary and I go back before we had kids and everything else when at the dawn of the internet age, he wrote a book, The Plot to Get Bill Gates. The Plot to Get Bill Gates.

Speaker 1 We were on a book tour with Poe Bronson, who wrote Newtis on the Late Chiff, and I had the paper rack version of AOL.com. And it was literally the dawn of the information age.

Speaker 1 And John Karp, who's now running Simon Schuster at the time, had this genius idea to send us on a tour around the country called the Bleeding Edge Tour.

Speaker 4 The Silicon Valley Bleeding Edge Tour.

Speaker 1 Right. And so I was there to say

Speaker 1 this was a great moment and huge fortunes would be created. It turns out I was right.
He was there to be sort of the contrarian, right? Is that?

Speaker 4 Sure, I'd point out that a lot of fortunes were lost and then eventually a few people were lost.

Speaker 1 Right, that's correct. So, and then Paul Bronson was there to be super earnest.

Speaker 1 And we had the best time and we went all around the country talking about this new internet. And it was, it was so weird to think about what's happened since.

Speaker 1 In any case, we were young then and so naive, but not really. He was never naive, he was sort of a pain in the ass everywhere we went.
But anyway, it was

Speaker 2 mutual. I think it was a good question.
Yes, you were doubtful.

Speaker 1 Yes, exactly. So, anyway, you've come out with a book about the current makers of money around the AI sector.
You're one of many.

Speaker 1 I've just been delivered three or four, mostly about open AI, actually. Yours is called AI Valley, Microsoft Google, and the trillion-dollar race to cash in on artificial intelligence.
Well-timed.

Speaker 1 Christy, you oversee Johns Hopkins Technology Ventures, which means you're responsible for shepherding university research from the lab to the private sector, including in AI.

Speaker 1 Both of you had a front row seat to the growing sector and a broadcast of characters, researchers, founders, entrepreneurs, incubators, VCs, and also these big tech billionaires. A lot of drama.

Speaker 1 So to start off, can even you describe the interplay between these characters and their relationships, this new field? Where's the most tension, affinity? Who do you relate to the most?

Speaker 1 Why don't you start, Gary?

Speaker 4 So, you know, it's interesting. I've always loved startups, venture capital startups, the gambling, that whole ecosystem.
So in the end of 2022, I said, wow, this AI thing is going to be a big deal.

Speaker 4 So I want to like, what startups are figuring out how to cash in on this moment? And so I want to go find what's the next Google? What's the next Facebook?

Speaker 4 And unfortunately, what I found is the next Google is probably Google when it comes to AI. And the next Facebook or Meta is going to be meta.

Speaker 4 That, you know, there's still all this opportunity to create nice, in quotes modest businesses you know tens of millions hundreds of millions of dollars but the next google those foundational companies that will be eventually worth a trillion dollars ai is so expensive that there's very few startups that really can afford to be playing you know

Speaker 4 basic stuff like text to video text to audio large language models i i really fear is going to be the stuff of the large corporate

Speaker 1 yes which is something i've talked about a lot this is a fear of mine is that this is not the same thing that when you and I were first these were a lot of startups you never heard of never heard of uber you never heard of Instagram they were real groundbreakers in this case it's too costly and also these companies have so much sway over compute that it's hard

Speaker 3 and the data and the data they have access to the data which is the new gold right yeah well I love talking about startups I was an investor and entrepreneur for a long time and we spin out about 10 new companies out of Hopkins research every year but I think what people don't necessarily realize is that the foundations of the tech industry actually come from a lot of the work that happens happens at universities that's not acknowledged.

Speaker 3 Think about how many of you actually got here by typing something into Google Maps or Apple Maps today or your Uber or Lyft or whatever. That foundational research of GPS,

Speaker 3 people don't necessarily realize or acknowledge that actually came from Hopkins researchers understanding Doppler satellite data 50 years ago and eventually in the 80s was allowed in the public domain.

Speaker 3 And here we are using it every day. And so the origins of so many of these tech companies come from what we're doing.

Speaker 2 Before.

Speaker 1 Before. Before, meaning the early tech, yes, Stanford or whatever, but it's less and less so.
That's been.

Speaker 3 I disagree.

Speaker 3 I think if you think about where so much of the technology continues to come from, it's not like when Open AI was founded 10 years ago that Sam and Ilan woke up and magically thought of AI.

Speaker 3 That was over decades and decades of research.

Speaker 3 And I think you're continuing to see that the types of faculty and students that are learning and training at places like Hopkins, but all over, they are bringing those

Speaker 3 technologies to market, whatever the next GPS is in the next decade.

Speaker 4 I think what you were trying to say is what's scary to so many of us, what must be scary to Silicon Valley, even if the leaders are being quiet about it, is cutting off basic research to universities, research for foundational

Speaker 4 exploratory research is so short-sighted. That's our magic formula in this country.
That's our secret sauce. You know, AI in the 50s, the 60s, the 70s, the 80s, it was government-funded research.

Speaker 4 And my big fear is, you know, the Chinese have said by 2030, they want to be dominant in AI. And if we're cutting off this basic research, I think we're giving them a big advantage.

Speaker 1 Yeah. Christy?

Speaker 3 Look, I agree. I think that

Speaker 3 to quote my former colleague David Singer, who just had an article this week.

Speaker 2 He's in the New York Times.

Speaker 3 In the New York Times, said, drawing a causal link between federal investment and basic science research and the rise of VC industry is about as difficult as reading a map.

Speaker 3 And so those connections are happening then, now, and in the future.

Speaker 3 And whether it's the AI winter that you described in your book from the 70s and 80s, when government stopped funding research into AI, the research stopped, the same thing could happen now.

Speaker 3 And it's just really important for us to acknowledge that.

Speaker 1 So talk a little bit, Chrissy, you play actually some of these roles, as you said, investment analysts. In the 2010s, you co-founded your own biotech startups.
And 12 years ago, you started this.

Speaker 1 Now, a lot of universities have these. What was it like to start that a dozen years ago? Because Stanford was already pretty active.

Speaker 1 Certain universities had already moved in that way. Stanford probably is the most famous, as Gary knows.

Speaker 3 Yeah, Stanford, MIT, a number of universities have a 50-year head start basically to what this is.

Speaker 3 When the dean of engineering at Stanford in the 70s looked out at Palo Alto and said, there's a field, we should probably put a research park there.

Speaker 3 And some of the first inhabitants of that research park were Mr. Hewlett and Mr.
Packard, right? They have the head start.

Speaker 3 Hopkins has always been around the foundational basic research, but I was hired to be a bit of a disruptor 12 years ago to come in and think about how we could commercialize the incredible things that come out of Johns Hopkins.

Speaker 3 So, that's that's the mission. I wake up every day and do that.

Speaker 1 So, Gary, in your book, you write about the early iterations. You and I have talked about this many times as Silicon Valley.
Obviously, Stanford is the unique role in that area.

Speaker 1 As you just noted, Google began also as a graduate student project there.

Speaker 1 Talk about sort of early research and incubator programs.

Speaker 2 Right.

Speaker 4 So, you know, dating back to the 50s, 60s,

Speaker 4 it was the students on the campuses, it was the professors who were giving rise to these companies.

Speaker 4 So in the 1980s, there were all these companies blooming around Stanford, MIT, a few other universities that were doing cutting-edge AI research. And, you know, actually a few of them went public.

Speaker 4 They didn't last. You know, there was a second AI winter after that.

Speaker 4 And that one was a little bit different because in the 80s, it was companies and it was venture capitalists that temporarily got into it.

Speaker 4 They were burned. And then it really wouldn't be until 2015, 2018, where the pioneering VCs were finally daring to put money into AI back then.
It would have been autonomous vehicles.

Speaker 4 But, you know, I mean, that's the Silicon Valley way. That's what I love about Silicon Valley.
You say Stanford, to me, it's just a startup machine. The whole place is almost built to create startups.

Speaker 4 You've got Stanford, which gives rise. You got UC Berkeley, which gives rise, but the venture capitalists out there and there's this whole ecosystem.

Speaker 1 From startups to venture capitalists to wealth to the

Speaker 4 mindset, like, why aren't you starting a company that they're hiring people from a Google American?

Speaker 1 Still today, that's the case.

Speaker 4 Yeah, I mean, yeah.

Speaker 1 In AI, it certainly is. It's in San Francisco now.

Speaker 4 Right. So one big difference between the 90s when we were starting off reporting and today is the center of gravity has moved from Silicon Valley to San Francisco.
It's not a coincidence.

Speaker 4 Open AI, Anthropic, a lot of the other big AI companies are in San Francisco. The venture capitalists used to famously be in all in Menlo Park.
They now have outposts in San Francisco.

Speaker 4 And again, I don't want to give the wrong idea. There's going to be a gazillion startups that have nice returns for venture capitalists, nice

Speaker 4 returns for their founders. It's more that stuff that's the innovation, the real innovation, the stuff underneath.
So there'll be apps on top of these foundational models, the basic stuff.

Speaker 4 But I really do fear that maybe OpenAI will break in, maybe Anthropic will break in. That's Claude, the LLM.

Speaker 4 But I actually think there's a pretty good chance, given how much money they still have to raise long before they'll ever show profit, that they'll just get bought up by a giant.

Speaker 4 And by the way, what's interesting is like Google could lose, like Gemini, their chatbot doesn't win, but they've invested billions of dollars in Anthropic.

Speaker 4 Microsoft and their co-pilot could lose, but they put, what, 15 billion or so into OpenAI.

Speaker 2 So even if they lose, they win. Correct.

Speaker 1 We'll be back in a minute.

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Speaker 1 so christy part of your mission is to make baltimore itself a tech hub um like silicon valley or boston it's really silicon valley and everybody else goes down pretty far as gary writes in the book um silicon valley is both a place and idea baltimore was one of the 31 federal tech hubs but was skipped over in the first two funding rounds.

Speaker 1 Who knows what's going to happen now? But talk about this idea, you know, when they had Silicon Beach, they had Silicon Prairie, they had Silicon Holler.

Speaker 1 I'd always, I would be like, no, this is not happening in any of these places. So talk a little bit about trying to do that in a place, because place does matter.

Speaker 1 Even in a digital world, place does matter.

Speaker 3 Yeah, no, I think it does. And

Speaker 3 look, at Johns Hopkins, one of the things that we are known for is biomedical research, a lot of the basic foundation models.

Speaker 3 You don't can look back to Bert Vogelstein, who several decades ago discovered the genetic basis for cancer. And since then, we have built on all sorts of therapies and diagnosis.

Speaker 3 What is the future of drug discovery? It's going to be AI. And we're making a huge bet, as you probably know, Kara, in AI at Johns Hopkins, the State of Science Institute.
Yes.

Speaker 3 Over 100 faculty, hundreds of students. Who is going to train the people that are going to work at those companies? It's going to be places like Hopkins.

Speaker 3 And so I think the types of companies that we spin out are those that are good for the world. We're not creating the seventh dogwalking walking app.
We're actually, you know, creating...

Speaker 2 Why not?

Speaker 3 I mean, are the first six not good enough? I mean, really, let's.

Speaker 1 Healthcare is really, oddly enough, my next book's all about this, but the AI and its impact on healthcare is obviously one of the biggest possibilities.

Speaker 3 Yeah, my friend Alec Ross, who wrote a book, Industries of the Future, said that the last trillion-dollar industry was created with the using zeros and ones, and the next trillion-dollar industry will be with the four letters of DNA.

Speaker 3 And there's no reason why Johns Hopkins and Baltimore will not dominate that. We already are.
We already have companies spinning out, raising billions of dollars.

Speaker 1 So according to Pitchbook, just for a little fact check here, though, venture capital investment in AI starters was up more than 50% last year, over $131 billion. Feels bubblicious.

Speaker 1 In the first quarter of the year, they say the AI sector raised $73.1 billion, but spoiler alert, $40 billion of that went to open AI.

Speaker 1 Both of you talk about this. You've raised about $4.4 billion, correct, Christine Venture?

Speaker 2 The total company.

Speaker 1 So, Gary, first, can you talk a little bit about this fact that it's sort of coalesced around really one company?

Speaker 4 Right. Well, a few.
So,

Speaker 4 XAI, that's Elon Musk's company. They're out there trying to raise, I think, 20 billion.
So, 40 billion would be. is the largest raise ever in venture history.
20 billion would be the second largest.

Speaker 4 You know, we covered the dot-com boom and bust, and we're seeing some of the same things. So Ilya Satskiver, famous co-founder of OpenAI, famous for initiating a coup that lasted five days at OpenAI.

Speaker 1 It's called a coupet.

Speaker 4 So, you know, after he left, started his own company, Safe Superintelligence, and, you know, they raised $2 billion at, you know, a paper worth of $30 billion.

Speaker 2 They don't have a product yet. Right.

Speaker 4 But that's venture capital, right? They slap down bets really fast. So, you know, some company writes a memo.
Two or three promising founders write a memo.

Speaker 4 A week later, they've raised tens of millions of dollars at 100 million valuation. Like it's really just rolling the dice, but that's the venture capital business.

Speaker 4 You know, most of these will not work, but a few of them will. And when they do, they'll show extraordinary returns.

Speaker 1 So, Chrissy, you said with these investments you're making, you co-founded Up Surge Baltimore network of entrepreneurs and startups to facilitate the fundraising.

Speaker 1 Talk about that environment because a lot of people definitely feel that there's an overinvestment in infrastructure, an overinvestment in the valuations.

Speaker 1 And this isn't like the internet because it was just an overinvestment in companies. It wasn't infrastructure.
It wasn't the costs were quite low, actually.

Speaker 1 Here, they're very high, whether it's energy or infrastructure or whatever. So the bits are slightly bigger in that regard, not just the money, the figures.
So, talk a little bit about that.

Speaker 3 Yeah, I mean, 80% of what we do is in the life sciences. Again, there's a heavy overlay now with drug discovery and AI on that, but it's been a bit of a biotech winter since 2021.

Speaker 3 So it's a little bit different. And it's also not necessarily Cara about the cult of personality.
It's much more about the research and what's behind it.

Speaker 3 And I would say those valuations and the money flowing there are much more down to earth. It reminds me a little bit, you had Josh Johnson on your podcast.
He's a huge fan.

Speaker 1 I'm the famous AI researcher and

Speaker 2 comedian.

Speaker 3 But he actually made a really, I thought it was really poignant about the idea that so much of our world today, the foundation of that was people who we will never know their names.

Speaker 3 We'll have forgotten that some of the things that we take for granted. And I I feel like that's so much of the research that happens at places like Hopkins.

Speaker 3 They're not necessarily going to get the same hype of a big AI startup in California, but there's the level of impact that one would have.

Speaker 3 So you can get ROI and you can bring great things to market, but it doesn't necessarily have to raise 30 billion and be on the front page to do that.

Speaker 1 Or have to deal with egos and things like that.

Speaker 3 Hopkins is a very low ego place. I get to deal with some of the smartest, most earnest folks in the world.

Speaker 3 And at the end of the day, what they care about is the impact, not their name in the headlines.

Speaker 1 Silicon Valley's got you covered for that. So still, still, after all this time.
Especially. Especially, really.
It's gotten worse, hasn't it?

Speaker 4 It's gotten much worse. Yeah.
It really has. Yeah.

Speaker 1 Yeah. Every human growth hormone they take, it's gotten worse.

Speaker 2 Anyway,

Speaker 4 I think other stuff, too, I've read. I've read.

Speaker 2 Yeah.

Speaker 1 No, they say it. It doesn't matter.
So I want to get deeper into AI, specifically the intersection of life sciences and this.

Speaker 1 Gary, you point out in your book, healthcare is the area that techno-optimus, and that's a term they use for people who are pro AI versus they're going to kill us tomorrow.

Speaker 1 They often use the proof point to bolster their position that AI will change lives for the better.

Speaker 1 In a recent 60 Minutes interview, Google DeepMind CEO and Nobel laureate Demis Hababas said he believes AI has the ability to cure all diseases within the next this is something they go.

Speaker 1 This is the problem.

Speaker 2 Okay, all right. Okay.
Wait, okay. Okay, sorry.
Do you think, talk about this?

Speaker 1 Go ahead. Go ahead.

Speaker 2 Go ahead. No, no, no.

Speaker 1 I'm sorry. I'll allow you to rant.

Speaker 4 No, I mean, this is, I would say the same about the early internet. Like, it will do extraordinary things.

Speaker 4 I am totally with both of you that AI and healthcare is going to to be extraordinary for humanity. The connections it's going to make, the vaccines, the treatments, all of that.

Speaker 4 But why do you have to say it's going to wipe out cancer in 10 years, which I have heard?

Speaker 2 Why do you have to say it's going to wipe it?

Speaker 4 It won't.

Speaker 1 I mean, it's sort of like saying self-driving is here today.

Speaker 2 10 years ago.

Speaker 1 Someone said that. I'm not going to say who.
No, but

Speaker 4 that one I believed.

Speaker 2 I really.

Speaker 1 He's been recently unemployed from Doge because

Speaker 2 they're coming.

Speaker 3 No, but it's like this is

Speaker 4 where Silicon Valley, the tech bros, get themselves in trouble. They're overhyping.
And like, AI has a problem. First off, they had really bad timing.

Speaker 4 AI hit at the end of 2022 when distrust among the public in big tech was at its peak.

Speaker 4 You know, the terms I like to use: there's the doomers who are convinced that laser eyed robots are going to take over and subjugate humanity.

Speaker 4 There's the Zoomers who want no speed bumps, just let us do our thing, China, China, China kind of thing.

Speaker 1 I mean, they're going to beat us.

Speaker 4 And, you know, I'm kind of more with this Reed Hoffmanism, a bloomer that, you know, like, I think I could do extraordinary things, but let's be deliberate about it.

Speaker 4 We're way ahead of where the public is. You know, Pew did a poll last year.

Speaker 4 The majority of people are fearful of AI. And, you know, here we're talking about a technology where humans are going to lose their apex status.
You know, they're going to be these agents.

Speaker 4 AI is going to be our personal assistants, these AI agents, which means they're going to know everything about us and privacy issues.

Speaker 2 Like, we're getting way, way ahead.

Speaker 4 And I think the Zoomers are making a strategic error by saying, like, there should be no regulation.

Speaker 4 We should just like do everything we can to accelerate, you know, to quote our vice president, stop with the handwringing and let's just start winning.

Speaker 2 Right.

Speaker 4 And I'm just scared they're going to get way ahead of where the public is.

Speaker 1 It is interesting because the, the, the, the ones who are terrified are also not so much fun at a dinner party.

Speaker 1 I was at one dinner party with a bunch of them and they were like, you need to stop Sam Altman because the human race is at risk. You need to stop him now.
Kara, you're the only one.

Speaker 1 And I'm like, that's the plot of the Terminator and I am not her.

Speaker 2 Like, I have no,

Speaker 1 it was like, they literally were counting the plot of the Terminator to me and I had to kill him, which I didn't.

Speaker 4 I think our problem is that Hollywood is defining what we should be scared of.

Speaker 2 Right.

Speaker 4 Whereas there's much more line of sight things we should be, you know, AI and surveillance, AI and warfare, AI manipulating people, autonomous AI. This stuff isn't ready.

Speaker 4 Humans still have to be in the loop. That's the stuff I wish we were debating.
That's the stuff I wish we were, you know, worried about and not like

Speaker 2 robots taking over.

Speaker 1 Many are. But Christy, life sciences obviously is John Topkin's superpower.

Speaker 1 But a lot of the things I'd like to hear about them because they are very AI-assisted tools for early detection, disease prediction.

Speaker 1 When I was with Vinod Kos, he was talking about drug interaction is something that kills a lot of people, the wrong drug interest.

Speaker 1 Obviously, diagnostic tools for respiratory introduced cancers. Where do you see the most most potential?

Speaker 3 One very specific area. I can think of several, but I think within cancer diagnosis, there are 30 million CT scans done on the abdomen every single year.

Speaker 3 Imagine a world where you could just, with a simple algorithm, detect pancreatic cancer, right?

Speaker 3 And we have researchers working on that to basically say there could be otherwise undetected pancreatic cancer.

Speaker 3 And I don't know how much you all know about that, but generally, by the time it's detected, it's a death sentence.

Speaker 3 And so, the idea of early detection, which again is something that a number of our researchers have been working on, because cancer develops over decades, not overnight.

Speaker 3 And so, the sooner you can find it, the sooner you can treat it.

Speaker 3 Another application that we're all parents up here that maybe you'd have appreciation of, we have a wonderful woman who's an ER doc who basically just saw parents exasperated having to bring their kids in for a strep throat test, right?

Speaker 3 So, she figured out.

Speaker 1 Yes, we did that yesterday.

Speaker 1 I did not do it. My wife did it.
She totally gets credit.

Speaker 3 So, think about a world where you take your phone camera, take a picture of the throat, and it says strep throat or not, and you get the prescription.

Speaker 3 You don't have to sit at urgent care on a Friday night. You don't have to come into the ER and wait 12 hours on a Sunday, but you basically get treated right then.
So, both of those applications:

Speaker 3 one is great quality of life, one is literally life-changing, life-saving. In both of those cases, those are real and happening.

Speaker 1 We'll be back in a minute.

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Speaker 1 So one of these R D-heavy startups aren't coming just out of universities, Demisvapasway just mentioned, co-founded Isomorphic Labs.

Speaker 1 It's a division of Google spun out of DeepMind to focus on drug discovery, for example. They not only have Google money, they recently raised outside VC money.

Speaker 1 This kind of internal RD used to come from universities.

Speaker 2 Gary, what impact does it have?

Speaker 1 And Christy, how do you build them into sustainable businesses if they're destined to be bought out by tech companies or big pharma as AquaHires? What does that look like first you, Gary?

Speaker 4 Well, I mean, corporations have been investing in cutting-edge research, Microsoft research. Google has a big research arm, IBM, for decades and decades.
So, you know, that's always been going on.

Speaker 4 It is interesting. Some of the biggest venture capitalists in Silicon Valley right now are the tech companies.
You know, Microsoft in 2023 was the number one VC just by dollars given.

Speaker 4 Nvidia, they invest in like 35 or so startups. So there is this, some of it is just so damn expensive.
Like, you know, we're talking about a $40 billion raise.

Speaker 4 A large venture capital fund in Silicon Valley is $1 billion.

Speaker 4 And that's a tiny fraction of when they need to raise. And so, you know, what we're seeing is the large corporations, big tech companies, Amazon, Apple, Google, are taking over that role.

Speaker 1 And Chrissy, what does that have?

Speaker 1 This idea that how do you make a healthy AI industry if there's consolidation in this way or if the research comes out of here because they weren't going to let it out?

Speaker 1 You want diversification, presumably, to be in a healthy.

Speaker 3 Yeah, absolutely. Look, one of the cool things about my job is I get to be a bridge between these basic science researchers and industry and be that front door for Johns Hopkins.

Speaker 3 And so we have a lot of examples where we took things and worked really collaboratively with it. There are actually a lot of good players out there.
We have a drug that is for men's health.

Speaker 3 It's called Pylarify. It's for diagnosing prostate cancer.

Speaker 3 And one of our clinicians described it as, if you wanted to treat prostate cancer before this incredible technology came out, it was like watching black and white TV in the 1950s and it's now Technicolor.

Speaker 3 And we licensed to a company in Boston called Lantheus. They sell it.
We get royalties on it. So everybody wins.
The patient wins.

Speaker 3 The company gets the growth and the roi we're rewarded with those royalties as something that we discovered so i i think just being that natural bridge over and over that does require care though that that we get credit for what we've done right so a lot of working with industry is maybe acknowledging that there were people that came before there were people that built this foundation and uh what does that look like and and how can we be more collaborative but the idea of these aqua hires there was obviously mustafa suleiman with inflection this was there were many ai companies started now microsoft essentially bought that company although it wasn't looked at that way.

Speaker 1 They bought, they hired everybody.

Speaker 4 They hired virtually every employee there and then gave $650 million as a licensing deal to like, you know, make everyone whole, make everyone happy.

Speaker 4 You know, my big concern on this is this idea of consolidation of power in just a few big tech companies.

Speaker 4 The Silicon Valley way, we've seen this forever, Cara, is that let's get you know, a few smart guys, and there are always guys, in a room and we'll figure out AI is different.

Speaker 4 It's, I mean, first off, it's not just computer scientists, it's math, it's linguists, but where are the sociologists and philosophers? Where's there a diverse group of people?

Speaker 4 Like, this stuff is powerful. It's reflecting humanity.
We're going to increasingly end up relying on it, you know, and everything for, you know, education, social life, business and all.

Speaker 4 And so that's my biggest worry, that that same mentality, Sam Altman and a few smart guys are going to show up at his mansion in San Francisco and they'll figure it out because they're smart.

Speaker 4 Yes, they're smart, but it's more than just being smart.

Speaker 1 Two more things before we have to go.

Speaker 1 The costs are so high, but DeepSee had scared the crap out of everyone. This is a Chinese AM model trained for less than $6 million.

Speaker 1 That's questionable if that was actually, but they called it a Sputnik moment.

Speaker 1 If there were more efficient models, Christy, with more chips, bring down the overall price of computing power, how would it impact small startups like you invest in?

Speaker 3 Well, I think there are a lot of parallels here to even the tech industry today because, what, 15, 20 years ago, you had to buy big servers and big computers and have all this infrastructure, and now it's accessible.

Speaker 3 So I think in the same way, these tools make the playing field a lot more level.

Speaker 3 And I think we'll be surprised with the level of disruption really across the board, not just in pure tech, but whether it's energy, whether it's life sciences, whether it's adjacents.

Speaker 3 I think there's a lot of room for innovation. And I think it's great that the...
the playing field will be much more level.

Speaker 1 So I want to finish up with two things. One is this threat to research that we talked at the top.
I'm going to ask you, Gary, I'd be happy to put you on the spot, Christy.

Speaker 1 The federal government is one of the biggest funders of university RD.

Speaker 1 A lot of Johns Hopkins money comes from the National Institute of Health, for example, about $1 billion in fiscal 2024, more than any other institution in the U.S.

Speaker 1 A lot of the money has been canceled, not just at Johns Hopkins, but elsewhere is in limbo.

Speaker 1 Why don't you talk about this, Gary?

Speaker 1 What's the you can talk about it, Christy, but what is the impact right now on research at places like Johns Hopkins if the Trump administration doesn't reverse course on this?

Speaker 4 Sort of is the way it happened.

Speaker 4 To me, it's like they bent over and unplugged the machine. And just so there's all this wasted stuff.

Speaker 4 And what really scares me, I was out in Silicon Valley a few weeks ago talking to people at Stanford. And like, it's not like you could just, oh, okay, this was this four-year period.

Speaker 4 Now let's just revive it. I mean, that, that to me is the big worry that a new administration would come in is more foresighted, like, you know, thinking like this, we really need this.

Speaker 4 And it's not going to be so easy to just like, oh, okay, well, we lost a few years, but now we're going to go back right back to where we were.

Speaker 1 How do you deal with it?

Speaker 3 Look, I think there are just a couple of things I would say here.

Speaker 3 I think what the NIH has done over time with very competitive grant funding to universities and ultimately to the commercial sector, this incredible group of three sectors that came together have lowered the deaths of heart disease and stroke by 75% in the last 40 years.

Speaker 3 When I was growing up, HIV and AIDS was a death sentence, and now you can live a normal lifespan thanks to NIH research into universities, into the private sector.

Speaker 3 And so I think we want to continue that. My greatest fear is for this incredibly talented generation of scientists that are going to choose necessarily not to do that work.

Speaker 3 If their grant funding is cut off, if they're unable to continue their work, they'll go do something else. And what does that mean for the next decades of that type of progress? Irreparable?

Speaker 2 Harm?

Speaker 3 I don't know.

Speaker 1 What they didn't invent. We don't know.
Correct. All right.
Last question.

Speaker 1 Speaking of chilling effect, Gary, at the end of the book, you write, AI was, of course, a bubble, but bubbles are as much a part of the Valley's boom and bust economy as underage Cockshore founders and the VCs who fund them.

Speaker 1 You used the past tense. So was it a bubble? Did it burst? Is there a possibility of another AI winter that some commentators have been warning about since last year?

Speaker 1 And Chrissy, how do you feel about the bubble and the winter?

Speaker 4 Yeah, no, I don't think the bubble has burst. There's still tons.
You just mentioned a stat, the 150 billion or whatever it is in venture capital going in

Speaker 4 AI.

Speaker 4 I mean, I think what we're going to see is a natural cycle where a lot of these companies are going to go out of business, but some will break through and will go on to have, you know, kind of long, great lives for a company.

Speaker 4 But it's mountains and valleys. It's like it's going to peak and then there'll be a slower period.

Speaker 4 I don't agree with people in Silicon Valley who say, like, oh, we have artificial general intelligence in the next year or two. I still think we're

Speaker 4 breakthrough to a way. They'll say they have AGI because their venture capitalists need them to say that.
And it's an amorphous definition kind of thing.

Speaker 4 But no, I think we're still in the middle of a prolonged, fruitful period for AI.

Speaker 3 For the VCs to get their return on investment here, we need the capital markets to open up, which means we need certainty, which means we need great investments.

Speaker 3 And the moment that a number of these investments go bad, it's not going to be great. I've been part of four downturns in my career.

Speaker 3 And you never know exactly when the top is, but you've got to hope that things go well, at least for a couple of bellwethers.

Speaker 2 Are you worried about that?

Speaker 1 What would make you worried?

Speaker 3 I mean, if companies aren't able to have exits, then

Speaker 3 it's hard for the VCs to tell their LPs, yeah, let's pour more money in.

Speaker 1 So last question, let's end on a positive note. If you could do one thing to fundamentally secure innovation in the AI space for the next generation of entrepreneurs, what would it be?

Speaker 3 Look, I think there has to be a combination of thinking about the products, the returns, and also the policy. I mean, look at this center.
This is where we can really be thoughtful.

Speaker 3 The companies don't necessarily have the best interest in humanity in mind. And so let's come together and figure out what the policy arguments are, what the future of that investment would be.

Speaker 1 But so I think having- What would be the key policy, you'd think, if you had to,

Speaker 3 I don't know. I'd have to ask our policy experts on that.

Speaker 4 You know, I do think there just needs to be a few ground rules that the Biden administration, I thought, had, you know, a gentle policy: like, hey, before you release these things, these powerful models, you know, we're going to require you to test them and share the results with us.

Speaker 4 And, you know, Silicon Valley really rebelled against that. Sam Altman was all for it until the Trump administration.
Now he's all against it, kind of thing.

Speaker 4 But I do think that we could put in some pretty modest regulations that make sure that the general public is there with these AI companies.

Speaker 4 You know, I'm convinced there's something bad is going to happen. I don't know what it is.

Speaker 4 A trillion dollars siphoned off from the global economic system, money system before a human even realizes it, whatever it is. And then people are going to panic and hate AI.

Speaker 4 And that's what I really want to prevent because I'm with you. You know, education, healthcare, science, I think extraordinary things could happen with AI.

Speaker 4 And I'd hate for there to be another AI winter because of the carelessness.

Speaker 1 Are these the critical years right now? And who's the most important person in this?

Speaker 4 The most important person. I would say policymakers in DC.
I don't know if it's a single person. Yeah, I think these are the formative years.
This is really going to shape people's idea of AI.

Speaker 4 And again, people are mistrustful of AI. And I'm scared that that's going to really slow things down, which

Speaker 4 would be a shame.

Speaker 1 Christy, most important person right now or a company?

Speaker 3 Yeah, I don't know that there's anyone, but I think just keeping an eye on the promise and the peril.

Speaker 1 All right, on that note, thank you too.

Speaker 2 Thank you.

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