Agents Of Tech

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Aug 2026

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*Where big questions meet bold ideas* Agents of Tech is a video podcast exploring the biggest questions of our time—featuring bold thinkers and transformative ideas driving change. Perfect for the curious, the thoughtful and anyone invested in what’s next for our planet. Hosted by Stephen Horn, former BBC producer turned entrepreneur and CEO, Autria Godfrey, Emmy Award-winning journalist and Laila Rizvi, neuroscience and tech researcher, the show features conversations with trailblazers reshaping the scientific frontier.

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August 13, 2026Episode 223 min

Are We Investing Too Much Money on AI? Oxford Economist Weighs In

Trillions of dollars are being poured into the AI promise of a global economic transformation. We're being sold a productivity revolution, faster output, greater growth, and unprecedented prosperity. But when? Real, tangible gains are barely showing up in the data. So is the return on investment actually going to be smaller and slower than promised? And could these billion-dollar bets bankrupt the companies making them and take the global economy down with it? This week, hosts Stephen Horn and Laila Rizvi are talking to globally renowned economist and Director of the Oxford Internet Institute, University of Oxford, Professor Carl Benedikt Frey, co-author of the famous “Future of Employment” study which garnered headlines about robots taking over our jobs and, more recently, his book "The Technology Trap," which showed that revolutionary technologies can take generations to pay off. Laila kicks off the interview by asking Professor Frey if he thinks that the trillions of dollars being invested in AI will pay off in the next five years. The more interesting question, he says, is whether it’s going to pay off for society and the economy. His answer to that one: probably not! He also questions how many of the firms investing enormous resources in the AI race will come out profitable on the other side. Next, Stephen and Carl compare the AI Revolution to the Industrial Revolution. Professor Frey points out that the first Industrial Revolution caused disruption, job loss and decline of wages at the lower end of income distribution, but that the second Industrial Revolution brought us broad-based improvements in standard of living and income. “I think a key question going forward is whether AI will mainly take the form of automation, and more closely mirror the First Industrial Revolution, or whether we will see it develop new products, new industries that create new jobs and activities, that create more broader shared abundance like the Second Industrial Revolution.” So where will innovation come from? Professor Frey points out the decline in breakthrough innovation and breakthrough patents since the computer revolution, which he attributes to an increase of projects, but at a lower quality. Laila asks whether access to AI is leading to more “AI slop” in academic papers, work and essays, while Stephen wonders how companies are actually going to innovate by putting AI at the heart of process. Laila asks Professor Frey what happens to society if AI doesn’t deliver productivity growth soon? Has AI done more harm than good? “Well, AI is clearly a tremendously unpopular technology… It's hard to think of any other technology where the developers of that technology are saying, "Well, best case, this will take your job, but worst case, it's going to kill you." Plus, he says, there’s plenty of hype because the stakes are so high. Stephen turns to the role of governments in the development of AI, and Professor Frey describes approaches that make sense for Europe, China and the USA. When will AI boost productivity and how significant will it be? Professor Frey thinks the answer “depends to what degree we'll see new firms coming in, building new products and new industries around AI” rather than AI merely being used as a productivity tool by individuals, and that “institutional change is going to be needed for these productivity gains to be realized.” As always, we end with our Rapid Fire segment. Laila asks Professor Frey for something that's universally accepted in his field that he strongly disagrees with: “It's seen as almost universally true that the best predictor for the future of work is what machine capabilities are. What's missing in there is that we need to consider more what humans actually want.” Finally, Stephen asks Carl what he’s optimistic about: “I'm really quite optimistic about the potential for AI in medical discovery. I think we do have an extraordinary opportunity for curing many diseases, including, hopefully, cancer.”

August 6, 2026Episode 126 min

The Man Who Ran America's $9B Science Agency: "AI Isn't a Private-Sector Miracle"

If public money helped build the foundations of AI, but now private companies control much of its future, who should be reaping the benefits? Is there a role for the government? Who should be in charge of shaping what comes next? In this episode of Agents of Tech, our hosts Autria Godfrey, Stephen Horn, and Laila Rizvi are talking AI, publicly funded science, and the future of innovation with former National Science Foundation Director, Dr. Sethuraman Panchanathan, aka Panch, the Professor of Technology and Innovation at Arizona State University. Our guest, Dr. Sethuraman Panchanathan ran the National Science Foundation in the United States, one of the most important and powerful public science agencies in the world. The NSF was investing in AI long before names like Anthropic or OpenAI even existed. According to Panch, over the last six decades, the NSF has funded 80% of non-defense compute research – even, as Laila says, during “AI winters.” We start off by asking, how can anyone make the argument that AI and the current AI structure that we know and use today did not come from the public? And what role should the government play in AI research, or in passing legislation that will put AI safeguards in place? Dr. Panchanathan tells us about the 27 AI institutes the NSF launched while he was there that permeate all aspects of AI. He talks about the exciting opportunity that the co-evolution of AI and humans represents. Stephen asks how we can make sure AI is for everybody and it doesn’t lead to greater inequality, given the trillion dollars hyperscalers are pumping into AI? Panch describes the importance of the democratization of AI access, and the role of the federal government in these efforts. He explains how the NSF built the National AI Research Resource (NAIRR), not only as a federal government project, but as a hyper-partnership approach that also leveraged all of the assets and infrastructure of the private sector. Laila points out that it goes further than just having access – it’s reaping the benefits of those tools. Panch agrees, and likens the situation to the “digital divide” we talked about with broadband access. “We have had these kinds of challenges in the past,” Dr. Panchanathan says, and the answer is “democratized access in the form of education, training, awareness.” He talks about how the NSF’s Regional Innovation Engine program brought stakeholders together in a synchronized manner. After the break, Autria asks Dr. Panchanathan whether the government has enough in-house technical expertise to try to evaluate frontier AI systems properly and keep up with the speed at which things are progressing. Yes, says Panch, but expertise isn’t enough. “You want to be partnering with the people who are in the frontier...they are the people in the industry. And I can tell you, I've had many conversations with leaders in industry. They are only too happy to partner with the government in helping shape all of this future together in an unselfish way.” As always, we end our interview with our Rapid Fire segment, where our hosts ask our guests a series of three questions. Autria asks Panch where people will draw the line when it comes to allowing AI in their personal lives; his answer: compromising their privacy. Laila asks him for something that's universally accepted in his field that he strongly disagrees with; Dr. Panchanathan talks about the future of learning and why he doesn’t agree “that somehow learning happens in the campuses and four walls by the faculty that are going to be training you.” Finally, Stephen asks him what he’s optimistic about. “I am very, very optimistic about what technology and the human can do to unleash the latent potential of humans. We have not seen anything yet, my dear friends.” What about you? Would you like to see private industry continue to captain the ship, or should government and federal research institutions regain a bigger role? Weigh in by dropping your comments below.

July 23, 2026Episode 1418 min

Is the AI Backlash Overhyped or Justified?

AI was supposed to make us better: faster searches, more productivity, wider knowledge, but it hasn't, and public sentiment is starting to shift. People are inundated with fake images and voices, AI-generated slop, unreliable answers, phony influencers, and a growing wave of content that looks real, but it may not be. At the same time, young people just entering the job market are asking whether AI will not only just take their jobs, but alter their ability to have a career altogether. Is the conversation around AI contributing to this ever-growing crisis of distrust and misinformation? As the tide turns on AI, could public backlash against Big Tech be reaching a breaking point? In this episode of Agents of Tech, our hosts Stephen Horn, Autria Godfrey, and Laila Rizvi explore whether AI is still a productivity tool ready to revolutionize the world for the better, or if it’s creating a crisis of distrust and leading to a wave of anti-AI backlash? In this lively discussion episode, our hosts grapple with the important debate about whether the public is turning against AI or maybe just against the companies deploying it. Or, to put it another way, “Are we heading toward Big Tech's big tobacco moment?” We start off with Stephen describing the CEO’s perspective into the agentic AI revolution, comparing the benefits of AI adoption with the negatives and the narrative around it. Autria and Laila debate Hollywood studio’s response to using AI. Is it a tool to be used for augmentation. Will it replace workers? Or is it reflective of a flawed communication strategy by the studios? Stephen compares the AI revolution to the Industrial Revolution and the lag between the actual change and the perception of that change - even bringing up the original Luddites. You’ll hear from previous guests on our show, like Emily Bender, who talks about AI’s impact on early jobs in a clip from our episode, “What AI Is — and Isn't: A Conversation with Emily Bender.” According to Laila, this is another example of Silicon Valley’s messaging problem, and she cites statistics that show that the AI trust gap is different in the U.S. than it is in China. Stephen pushes back on Emily’s concern that replacing entry level jobs with AI will create problems up the corporate ladder in the future. But when Autria asks him if he’s predicting the end of the C-suite, he says that AI will lead to a fundamental restructuring of the workplace. We also explore the impact of AI on politics, citing our previous guest Gary Marcus. Will anti-AI sentiment be an important element in our upcoming political discourse? Next up, we share a clip with Wikipedia founder Jimmy Wales from our episode, “Wikipedia, Media Bias and AI with Jimmy Wales.” We discuss the relationship between AI and human knowledge, and whether LLMs would be completely lost without humans. In our final thoughts, Autria, Laila and Stephen debate the concept of “AI for good” and the role of public perception of AI. Stephen concludes that because the fundamental change created by AI will affect all aspects of society, it’s society, however you define it, that will have to grapple with it. And Laila brings in the importance of capital markets and their strategy of selling fear as opposed to highlighting the value-add. What do you think? Tell us in the comments below.

July 9, 2026Episode 1326 min

AI and the Economics of Doing Good with MIT’s Hala Hanna

Hundreds of billions of dollars are flowing into AI investment, yet our guest, Hala Hanna, Executive Director of MIT Solve, points out that less than 1% of AI venture funding is going to social impact. If AI is so powerful, why is so little of that power being directed toward the people and the problems that need it the most? Can AI really be used for the public good, or is the promise of AI for good just Silicon Valley lip service? We're talking AI, social impact, and the economics of doing good this week on Agents of Tech. According to Hala, “technology can and should serve humanity's most pressing needs, not just its most profitable markets. We think technology can close gaps in health, in wealth, in learning, and of course respond to climate change.” Solve was created a decade ago by the President of MIT, with the idea that “the challenges of our time require us to stretch a hand to all problem solvers around the world.” But because, according to Hala, “tech doesn't change the world, people do,” Solve finds incredible early-stage ventures that are using technology to close those equity gaps and helps them scale. Their global network of over 460 solutions reaches 430 million lives, with almost $90 million in direct funding and over $1.4 billion raised. Within their portfolio, AI-enabled ventures have been shown to reach 5 times more people than non-AI ventures. Hala tells us about one of their Solvers, a startup called Speetar that used AI to connect anyone with a smartphone to a doctor at a time where there was no digital healthcare infrastructure, and has since become the backbone of most of Libya's healthcare system. Hala and our hosts Autria Godfrey, Stephen Horn, and Laila Rizvi discuss why such a small percentage of the trillions of dollars being spent to build out AI goes to social causes. They also ask Hala what can be done to change that imbalance given the profit goals of the large hyperscalers and their responsibilities to their shareholders, as well as how to include the parts of the world that are being left out, like developing countries in the Global South. Among other things, Hala talks about how the “broligarchy” is employing a calculus that is completely disassociated from the real life of actual people, and that, “It’s also the regulatory wild west. Today, a deli sandwich is more regulated than AI.” Rather than wait for their conscience to kick in, Hala says, we need to structure incentives that get them to change, or to build alternative models. “We're back to prioritizing now cheap energy over clean energy, basically burning barrels of oil over micro doses of dopamine.” After our break, we cover how AI is being used with increasing frequency to not only assess grant applications, but also to write them. Hala explains how Solve is using AI, and why in their process, humans have the final say. We also explore how the “agentic revolution” is happening at a much faster rate than the industrial revolution, and how society can participate in imagining – and bringing about – a better future for everyone. As always, we end our interview with our Rapid Fire segment, where our hosts ask our guests a series of three questions. Autria asks Hala where people will draw the line when it comes to allowing AI in their personal lives; Laila asks her for something that's universally accepted that she strongly disagrees with; and Stephen asks her what she’s optimistic about. What about you? Do you think that AI should be pushed toward prioritizing more social good? Or are the commercial incentives simply too strong to sway current priorities? Can big tech help solve global problems? Or should we stop waiting for companies to do what only governments, funders and public institutions can mandate? Tell us in the comments below, and please be sure to like and subscribe.

June 25, 2026Episode 1226 min

Understanding AI World Models with AMI’s Alex LeBrun

The current AI boom is really built on the huge scaling of LLMs, but are they're getting it wrong? Alex LeBrun, Co-founder and CEO of Advanced Machine Intelligence (AMI Labs), argues that if AI is going to understand the world, model cause and effect, and reason across time, it may need something different: world models. His new company has raised more than $1 billion to try to prove it. He isn't making a technical argument – it's a challenge to the whole logic of the current AI boom. So today, our hosts Autria Godfrey, Stephen Horn and Laila Rizvi are asking Alex LeBrun whether the industry is over-invested in scaling just one dominant paradigm, and whether world models are a real alternative or just a better theory in a market that may have already chosen its winner. Autria kicks things off by asking Alex where LLMs fall short. According to Alex, “If you want to understand or manipulate the real world, then LLMs are not good…across the board.” He explains that LLMs are good in language-first tasks: everything that is discreet and recognized, like mathematics, coding, and information retrieval. But that’s “only one class of problems. It's not everything in the world.” Alex defines what world models are, how they are trained, and what they are most useful for. You’ll hear about how AMI chairman Yann LeCun and his team developed a concept called JEPA, Joint Embedding Predictive Architecture, which is a way to train world models through self-supervised learning. We explore how AMI is using data-rich video to train their world models – and why the vast majority of the videos on YouTube just won’t cut it as source material. Laila asks about the possibility of scaling LLMs to the point where they can lead to implicit world models emerging. Alex disagrees. He points to diminishing returns on the core progress of LLMs in spite of spending billions of dollars. Instead, he sees world models as complimentary to LLMs the way physicians need real world training in addition to only reading books. He even compares how LLMs and world models can mirror the way the human brain works, with different areas of the brain responsible for different functions while working together in tandem. We dive into the economics of AI, from whether further investment in scaling LLMs makes sense to whether world models will catch on with investors. Alex shares some of the challenges he’s faced competing for compute and raising capital in an investment climate where Anthropic has a $30 billion run rate. When Stephen wonders how world models will fit in with agentic AI in areas like healthcare, Alex points out that agentic models are very brittle and usually break when it comes to long term planning. He says 99% accuracy isn’t enough if it means killing 1 out of every 100 patients. World models build an internal representation of the world, which is more deterministic, and will allow for longer term planning with more accuracy. Finally, it’s time for our Rapid Fire segment, where our hosts ask our guests a series of three questions. Autria asks Alex where people will draw the line with AI in their personal lives; Laila asks him for something that's universally accepted in his field that he disagrees with; and Stephen asks Alex what will happen in the future that people aren't talking about now. In our post interview discussion, Autria. Stephen and Laila discuss whether Alex made the case for including world models in the AI economy, including diverting some of the capital being invested in LLMs into world models. We also want to know what you think. Are world models the new frontier in the world of AI, or is scaling LLMs still the best bet forward for seeing all the potential that AI has to offer? Tell us in the comments.

June 11, 2026Episode 1127 min

AI and Productivity at Work with Microsoft’s Matt Firestone

AI at work has been sold as a productivity revolution. But Microsoft's latest Work Trend Index points to a more complicated story. Workers are using AI, companies are buying the tools, and agents are beginning to take on more of the execution – but many organizations are not yet built to capture the value. The question is no longer just whether AI can help people work faster. It's whether companies know how to redesign work around it. Is enterprise AI finally moving from experimentation to measurable impact? Or are companies still buying the promise before they know how to change the work? In this episode of Agents of Tech, we’re talking the current state of AI, workplace productivity, and the rise of agents with Matt Firestone, General Manager of Frontier Function at Microsoft, who leads their work on Microsoft 365 Copilot and agents. Matt, who has been closely involved in the company's research on “The Frontier Firm,” starts off by explaining how Microsoft looked at native AI companies to figure out how existing companies can reorganize themselves to be more like them and reach the frontier. He and hosts Autria Godfrey, Stephen Horn and Laila Rizvi discuss frontier professionals, who use AI agents for multi-step workflows and multi-agent systems. According to the Microsoft Work Trend Index, 16% of people self-identify that way. Matt describes the “Transformation Paradox,” where frontier professionals do great things using AI but get resistance from their organizations. The group talks about how the rate of change is much faster than the Industrial Revolution, and how leaders need to listen to and rely on their employees who are ahead of them on the AI adoption curve. They explore barriers to adoption, including technology hurdles, organizational culture, and the relationship of individuals to AI and their career development. When Autria brings up the topic of AI replacing entry level workers, Matt points to data from the MWTI that shows people are using AI for different things than rote, repetitive task work. “49% of the usage of M365 Copilot was higher-order cognitive tasks. So things like deep data analysis, asking multi-shot questions, more sophisticated types of long-running research.” Which means, Matt says, that AI is actually raising the level, scope, breadth, and depth of what an entry level worker can do in their first few years. Laila asks Matt about how to make AI less of a product, where employees use it within limits, and more of a substrate, where frontier professionals can use AI to extract the most value out of it. Matt says that AI is “raising individual ambition” and shifting from task-based knowledge work to outcomes-based thinking. Stephen and Matt talk about how enterprise AI can unlock value in employees as well as organizations. Matt describes the 15x year-on-year growth in agents across their ecosystem, and explains Microsoft’s Customer Zero Program. In our Rapid Fire segment, Autria asks Matt about where people will draw the line with AI in their personal lives; Laila asks Matt for something that's universally accepted in your specialist field that he disagrees with; and Stephen asks Matt what he’s optimistic about? Be sure to stick around for the conversation between Autria, Stephen and Laila about how not every company is as engaged with AI adoption as Silicon Valley may think, and what the cost of falling behind may be. What are you seeing in your offices? Is AI at work genuinely changing how your organization operates, or is it still mostly experimentation at employees' leisure? If you are using AI tools, where are you seeing the real value? Has your company successfully implemented AI strategies that are supported by the proper framework, or are they woefully under-prepared on how to execute AI in your industry? Share some successes and some shortcomings in the comments below.

May 28, 2026Episode 829 min

Who Controls Medical AI and What Do They Want?

Disclaimer: The conversation in this video is for information purposes only and does not constitute medical advice. Always consult a licensed healthcare professional before making any health decisions. Dr. Eric Topol believes AI can predict disease decades before symptoms appear, and he's argued that AI without doctors may outperform doctors with AI. But someone has to set the rules. And right now, the people most likely to write them aren't doctors or patients, but rather insurers, tech companies, and hospital administrators. This week on Agents of Tech we’re exploring prevention, power, and who really controls medical AI, with Dr. Eric Topol, cardiologist, bestselling author, and Founder and Director of the Scripps Research Translational Institute. We start with Autria asking Dr. Topol about accountability when it comes to medical AI. “It has to be accountable,” he says, but “The problem most people don't realize is there's lots of errors by physicians. In the US, you know, 800,000 serious diagnostic errors a year that result in disability or death. So we're trying to improve accuracy.” He describes studies that show that AI without doctors outperforms doctors using AI, and offers some possible reasons that the studies came to those conclusions. Stephen brings up a Swedish breast cancer study that showed a 29% improvement when AI was brought into detecting, and wonders why that kind of result hasn’t led to widescale adoption. Eric breaks down some of the issues with adopting AI, and he and Laila discuss the implementation problem, including the impact of income disparities on access. Dr. Topol and Autria consider the differences between using AI to look for a cure, and what Eric feels is more promising, using AI for disease prevention. He explains to Stephen how AI has already been used not only to predict disease, but also when it will show up! With Alzheimer’s, for instance, there are layers of data we’re not yet using. “There are biomarkers, like the breakthrough one for Alzheimer's disease, p-tau217, that tells us in advance 15 or 20 years about people who are destined to have a high risk.” Later, he goes into more detail about how AI can make a difference in preventing Alzheimer’s and slowing down the “brain clock.” The conversation shifts to who controls medical AI and what their goals are. Dr. Topol describes hospital administrators who only want to use AI to “increase revenue and to use AI to maximize productivity…My biggest concern about the AI era in medicine is we have this great chance to restore a remarkable patient-doctor relationship. We may never see it again for a long, long time, if ever. And we could blow it because of business-centric issues.” Eric tells Laila how AI can give doctors back time they spend on writing notes, and how China is using opportunistic AI – finding things that were not the reason why abdominal and chest CTs were done that doctors miss – to pick up pancreatic cancer before it’s too late. Another question the team addresses is whether medical AI will benefit everyone or just the rich. Dr. Topol says it will be hard work to ensure the democratization of healthcare, and that it’s one of his primary worries. Finally, it’s our lightning round. Autria asks where people will draw the line with AI, and Eric talks about the current public backlash to AI that he feels will fade over time as we resolve their issues. Laila asks Eric what’s widely accepted in his field that he disagrees with, and he says it’s ludicrous that people in genomics say we shouldn't be using polygenic risk scores. And Stephen asks Eric what people aren't talking about now, and he says “no one's really talking about this prevention opportunity. I'm kind of the lone wolf out there.” What about you? If AI could predict your disease decades early, but the price was that that data is sitting in the hands of tech companies and insurers, not necessarily your doctor, would you still want it? Tell us in the comments.

May 14, 2026Episode 927 min

Can AI Create Materials That Never Existed?

CuspAI was co-founded by Max Welling, a pioneer of modern AI. But instead of building chatbots, he's creating and harnessing AI to discover new materials for everything from carbon capture to water purification, plastic alternatives, and more efficient batteries. If this works, it could change everything. But can it? And if so, when? This week, we’re talking science, scale, and when CuspAI will be able to deliver with company co-founder Max Welling. Before talking to Max, hosts Autria Godfrey, Stephen Horn, and Laila Rizvi discuss why AI-powered material creation is so exciting… and why it needs to be addressed with little skepticism. Autria kicks off the interview by asking Max Welling about CuspAI’s plans for 2026. He explains that they’re currently building their platform, and that 2026 will be a time when CuspAI will work with customers to actually design new materials, synthesize them, and put them into the real world. In 2026, they also plan to connect to high throughput self-driving lab experimental facilities to increase the speed of experimentation. Max unpacks how the process works, starting with a customer describing the real world material that they need, to AI agent assessment to see whether anything already exists that can fit the task, to generating entirely new materials that never existed before. They typically generate “hundreds of thousands to millions of those,” which are first tested by a digital twin consisting of very cheap property predictors that quickly assess whether the material could exist in this world, followed by sophisticated molecular dynamic simulations to assess their actual properties in high accuracy. Laila and Max discuss the process of verification in materials science, and Max lays out some of the difficulties that lay between moving from simulation into the real world. Autria asks where their first successes will come. Max predicts that it will be in the semiconductor space, partly because of partners like Hyundai that have the need and the capability to manufacture the new materials at scale. Max explains how semiconductor lithography has gotten to a point where they’re creating the smallest structures possible that are the size of a few atoms, and in order to make that happen they really need new materials. “We are now creating chips that grow in the third dimension, so they become sort of taller.” Stephen brings the conversation around to scalability, the importance of finding partners that can power that growth, and whether that scale can even happen in Europe. Max makes a distinction between making materials at scale and scaling up a company, but says that Europe has the right companies for both. Laila raises the issue of commercial demand versus public good, and Autria asks about the pressure around using AI for the betterment of humanity. Max’s answer: “For me, the only reason I do this is because I do want to make a positive net impact… And I think the same goes for all our employees.” As always, we end with our lightning round questions. Autria asks where people will draw the line with AI, and Max says “We don't want AI to invade our privacy. We don't want AI to be used for mass surveillance. We don't want AI to get us addicted. We don't want AI to manipulate our opinions.” Laila asks Max for something that's widely accepted in his field that he disagrees with. He says that AI superintelligence replacing humans might be a little overhyped at this point. And Stephen asks Max what will happen in 18 months that people aren't talking about now? Max’s answer: “That we can design materials that feel quite exotic right now, with properties that you could not imagine, and it could completely change the world, and hopefully for the better. That's what we are shooting for.” What do you think? Will CuspAI be able to deliver on their promises in 2026? Will AI help us create new materials that benefit humankind in less than a year? Or will it take them longer? Tell us in the comments.

April 30, 2026Episode 830 min

Will AI Help You Live 50 More Years? Immunologist Derya Unutmaz Weighs In

If you survive the next five years, says immunologist Derya Unutmaz, MD, you will live for the next 50 thanks to what AI can accomplish in medical research. But is AI really a silver bullet that solves humanity's most difficult problems? Or does that kind of thinking get us into trouble? Professor Unutmaz is an NIH funded immunologist, with 35 years of published research and more than 100 papers, and a professor at the Jackson Laboratory. But when it comes to what he calls the bio singularity – the moment when the convergence of AI and biotechnology radically transforms human biology – he’s ahead of most of his fellow scientists and researchers. He’s also ahead of most predictions about AGI and SGI, or super general intelligence, by at least a couple of years. But what if he’s right? “In the US alone,” Dr. Unutmaz tells hosts Autria Godfrey, Stephen Horn, and Laila Rizvi, “there are 12 million misdiagnoses every year. Only about 70% of the diseases are diagnosed correctly by medical professionals. And about 700,000 people die or…become sick because of misdiagnoses, okay?... with AI, if you could improve that by 10%, you are saving hundreds of thousands of lives.” For Derya, AI means the totality of artificial intelligence, including LLMs, agentic systems, world models, and more. He likens the different areas of AI to how the human brain works, with different areas managing different tasks. He even likens his own scientific training to the way AI models are trained. He explains that there are multiple levels of Artificial General Intelligence, where general means that you can generalize knowledge. “So for example, if you learn something on one topic, we can somehow generalize that information to learn something completely different, or understand something completely different.” According to Derya, we’re already at AGI Level One, where a system is as good as the top 1% of humanity. He thinks there will eventually be three or four levels of AGI. Dr. Unutmaz says that when Demis Hassabis is describing AGI, he really means ASI, or Artificial Super Intelligence, which is better than human. Derya says that “the Einstein test” – where we train an AI models on pre-1911 knowledge and ask it to recreate the General Theory of Relativity – is unfair, because only a few people in the history of humanity have been capable of such incredible insight. He also thinks we’ll reach a point where most jobs that depend on using a “computer, or your intelligence, or your experiences or expertise could be eventually replaced by AI.” In fact, he says, “I’m a scientist…and I can tell you that current AI models like GPT-5.2 PRO [are] simply better than me.” Derya says we are going to have to rethink the whole fabric of society and the impact of AI will be extremely disruptive. Whether he is right or not about the timeframe for AGI or the bio singularity, Stephen, Laila and Autria agree that the disruption from AI is here, now, and not enough people are addressing it. Do you think AI will cure disease within a decade, or that it's just a dangerous thing for a scientist to claim? Tell us in the comments. CHAPTERS: 00:00 - Will AI Cure All Disease Within 10 Years? 00:55 - AI, Disease and Bio Singularity with Immunologist Derya Unutmaz 01:21 - Why So Much Hype and Uncertainty Around AI and Science? 03:06 - We Can’t Leave This to the Scientists… or the Tech Bros 03:55 - If You Survive the Next 5 Years, Will AI Help You Live 50 More? 04:20 - Tech and AI Capabilities Doubling Every Few Months 07:31 - What is AGI? 08:44 - Are We Prepared for the Biggest Transformation in History? 10:48 - Is AI Hitting a Wall? Is the Einstein Test Fair? 13:58 - I’m a Scientist, and GPT-5.2 PRO Is Better Than Me 15:29 - Will There Always Be a Place for Humans In Science? 18:17 - Is It Unethical for Doctors and Scientists Not To Use AI? 24:55 - Aging Can Be Reversed Says Dr. Derya Unutmaz 25:26 - Is There Any Point in Publishing Scientific Papers Now?

April 16, 2026Episode 729 min

Wikipedia, Media Bias and AI with Jimmy Wales

As AI gets more capable, will it make public information more trustworthy, or less? Does news media have to be biased to be financially successful? Is AI a threat to Wikipedia or will we always be reliant to the human component when it comes to seeking trustworthy information? These are timely questions about AI, information, technology and trust that affect us all – which is why Stephen Horn, Autria Godfrey and Laila Rizvi are interviewing the founder of Wikipedia, Jimmy Wales. We start with a discussion of trust about where we get our information, and how to build trust amidst the changing economics of news media and AI. With Wikipedia celebrating its 25th Anniversary, Autria asks Jimmy how they overcame the public’s initial distrust and what he thinks about the current cynicism towards AI. He admits that “There is, you know, a cycle that happens…when the quality is low and something's very new, then people obviously are skeptical and quite reasonably so.” Laila asks if we’re close to AI superintelligence, and Jimmy explains that he’s a tech geek but not an expert in AI. The people he listens to, his friends Gary Marcus and Demis Hassabis, think we need some fundamental breakthroughs before that. Of course, he says, they may be wrong and things are moving pretty quickly. “It’s a classic sort of thing in tech, it’s an old saying: People tend to overestimate the short run and underestimate the long run.” The conversation turns to the value of neutrality and unbiased information. Laila suggests that people are happy with the ease of the answers they get from AI or social media and don’t have the luxury of researching every issue. Jimmy offers an “imperfect” analogy to junk food, saying “Junk food’s easy. Tastes really good right now… So I don't buy [crisps]. I don't like to have them around because… I actually do have a higher order sort of brain.” Stephen points out that the media world seems to be moving beyond providing multiple perspectives on an issue, and that there is no business model for neutrality. Jimmy disagrees, citing Wikipedia’s popularity, which is higher than the top 10 newspapers combined, and suggests that, when it comes to neutrality and fighting bias, “We have to fight for it.” In our rapid fire segment, Autria asks where people will finally draw the line when it comes to AI. Jimmy cites OpenClaw and his feeling that people will draw the line between using AI to get things done and the improper use of personal information by that AI. Laila asks Jimmy what's something that's universally accepted in his field that he disagrees with? His answer: “That news media has to be biased to be financially successful,” although he admits, “I'm a minority viewpoint there.” Finally, Stephen asks what Jimmy sees in the future that we’re not talking about today? Jimmy says we’re focused a lot about AI in LLMs, but there are other things going on like advances in biology, drug discovery, driverless cars and other positive, transformative developments that deserve more attention. “I think there's a lot more that's going to come that's going to be really pretty amazing.” CHAPTERS: 00:00 - Introduction 01:00 - Is Trust in Ai, Tech and Media in Short Supply? 04:10 - Early Skepticism about Wikipedia and AI 05:34 - When and Where To Use LLMs and AI 06:40 - Jimmy Wales on AI: Pretty Terrible at Facts but Kind of Creative 07:17 - Can AI Work With the Right Framework? 10:04 - Will AI Replace Wikipedia? 13:22 - The Seven Rules of Trust - Neutrality and Bias 15:18 - People Tend to Trust Individuals Over Abstract Entities 16:22 - Echo Chambers, Convenience and Trust 20:43 - Media Literacy and the Economics Of Trust 22:23 - Is There a Media Business Model for Neutrality? 24:19 - Drawing the Line Between Personal Info and Getting Things Done 25:14 - News Media Doesn’t Have to Be Biased to Be Financially Successful 25:38 - Bright Future for AI in Biology, Drug Discovery, Driverless Cars, More 27:11 - Can AI and Wikipedia Coexist?