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Lawrence Krauss welcomes Scott Aaronson to the Origins podcast, praising his remarkable intellect and contributions to quantum computing and AI safety. Aaronson, a leader in theoretical computer science, discusses his journey from winning the Waterman Prize to exploring the complexities of quantum computing and AI. He emphasizes the importance of understanding computational complexity and its implications for both fields. The conversation delves into the nature of quantum computing, highlighting its potential to solve problems that classical computers struggle with, such as factoring large numbers through Shor's algorithm. Aaronson explains that quantum computers operate on qubits, which can exist in superpositions, allowing them to perform calculations in ways that classical computers cannot. He also discusses the challenges of achieving fault-tolerant quantum computing and the significance of quantum error correction. As the discussion shifts to AI safety, Aaronson distinguishes between AI ethics, which focuses on the immediate societal impacts of AI, and AI alignment, which concerns ensuring that advanced AI systems act in accordance with human values. He notes the tension between these two perspectives and the need for a scientific approach to address the complexities of AI. Aaronson shares insights from his work at OpenAI, particularly on watermarking AI outputs to combat misinformation and misuse. He emphasizes the importance of developing methods to identify AI-generated content while acknowledging the limitations of current approaches. The conversation concludes with a reflection on the transformative potential of AI, likening it to past technological advancements while recognizing the unique challenges it presents. Throughout the podcast, Aaronson expresses a mix of optimism and caution regarding the future of AI, advocating for proactive measures to ensure its benefits while mitigating risks. He highlights the need for ongoing dialogue and research in AI safety and the importance of understanding the implications of these technologies for society.

In this episode of the Into the Impossible podcast, host Brian Keating interviews John Preskill, a prominent physicist known for his contributions to quantum computing. They discuss the essence of quantum computers, which utilize quantum mechanics to solve specific problems more efficiently than classical computers, particularly in understanding complex quantum systems. Preskill emphasizes the importance of entanglement in quantum computing, describing it as a frontier for scientific exploration. The conversation touches on the Church-Turing thesis, which suggests that a universal computer can simulate any physical process. Preskill argues that quantum computers could update this thesis, allowing for efficient simulations of nature's processes. He acknowledges the current limitations of quantum computing, noting that while they excel in certain areas like cryptography and simulating quantum systems, their full potential remains to be discovered. Preskill also addresses misconceptions about quantum computing, asserting that it is not limited to cryptography and that its applications could extend far beyond current understanding. He highlights the need for more powerful quantum computers to unlock new discoveries in materials science and chemistry, although he cautions that significant advancements may still be decades away. The discussion shifts to the concept of quantum supremacy, which Preskill defines as a quantum device performing tasks beyond the capabilities of classical computers. He recounts Google's 2019 announcement of achieving quantum supremacy, where their quantum computer completed a specific task much faster than classical supercomputers. As the conversation progresses, they explore the relationship between quantum mechanics and cosmology, touching on topics like black holes and the nature of reality. Preskill shares insights from his experiences with Stephen Hawking and the ongoing debates about information loss in black holes, suggesting that quantum mechanics may provide answers to these profound questions. The episode concludes with Preskill offering advice on maintaining a sense of humor and humility in science, emphasizing the importance of being open to new ideas and experimental evidence. He reflects on the value of understanding both theoretical and experimental aspects of physics, encouraging future scientists to bridge the gap between the two.

Deep Prasad, a 26-year-old founder of Quantum Generative Materials, reverse engineers UFOs using quantum computers and has raised 15 million dollars for the startup. He cites Pentagon sightings with five observable properties—instant acceleration, hypersonic speed with no signatures—and argues these point to macroscopic quantum behavior rather than ordinary physics. He believes advanced materials underlie UAPs and that quantum modeling could identify them. To achieve this, the team uses quantum computing simulations to model complex materials, since the Schrödinger many-body equation scales badly on classical machines. They describe qubits, superposition, and entanglement as essential to representing atomic systems. They also discuss quantum sensing and potential impacts on AI, encryption, and cryptocurrency.

Lawrence Krauss and Sabina Hossenfelder discuss recent scientific developments, beginning with the pervasive hype surrounding quantum computing. They critique companies like Quantum Motion and Fujitsu for making grand claims about mass-producible, scalable quantum computers without demonstrating actual functional systems or addressing fundamental challenges like quantum coherence and noise. Hossenfelder notes the disconnect between press releases, inflated stock prices, and the actual scientific progress, emphasizing the need for concrete data over speculative announcements. Krauss highlights the immense practical difficulties in building robust quantum computers, which involve isolating qubits, maintaining coherence, and managing noise, all at the limits of current technology. The conversation then shifts to the concept of warp drive, sparked by a National Geographic article. Both hosts express extreme skepticism, with Krauss detailing the theoretical requirements of Miguel Alcubierre's warp drive, such as negative energy and galactic-scale energy consumption, which are currently deemed impossible or impractical. He also points out the logistical paradox of setting up a warp drive path faster than light. Hossenfelder clarifies that while warp drive solutions exist mathematically within general relativity, they often require unphysical conditions. They agree that such discussions, while amusing, remain firmly in the realm of wishful thinking rather than realistic physics or engineering. Next, they address the 2023 Nobel Prize in Physics awarded to Geoffrey Hinton and John Hopfield for their work on artificial intelligence. Hossenfelder acknowledges claims of plagiarism by Jürgen Schmidhuber, noting that while the laureates might have been careless with citations, the Nobel Committee likely selected them because their work, particularly with Boltzmann machines and Ising models, could be framed within physics, adhering to Nobel's will. Krauss emphasizes that Nobel Prizes often recognize impactful work that shifts research directions, rather than just initial ideas, and that the committee works diligently to ensure accuracy. They also discuss the 2023 Nobel Prize for macroscopic quantum tunneling in superconductors, highlighting its demonstration of quantum mechanics on larger scales and its potential for quantum technologies, despite the term 'macroscopic' being somewhat misleading regarding the actual size of the devices. This work, though recognized decades later, is crucial for quantum engineering. Finally, the hosts delve into astrophysical phenomena. They discuss the concept of 'dark stars,' hypothesized to be powered by annihilating dark matter in the early universe, with recent James Webb Space Telescope data offering potential candidates. Krauss expresses skepticism, viewing it as particle physicists inventing solutions for astrophysical problems, requiring highly specific and potentially suspicious dark matter properties, and relying on weak observational signals. Hossenfelder, while open-minded, acknowledges the historical pattern of exotic theories explaining anomalies that later turn out to be normal phenomena. They conclude by discussing long-duration gamma-ray bursts, which are theorized to be caused by black holes eating stars from the inside. This explanation, while exotic, is considered less speculative than dark stars, as it involves known physics in a complex, albeit unusual, cosmic environment, demonstrating the universe's capacity for surprising events.

Hartmut Neven, leading Google Quantum AI, explains that quantum computers utilize quantum physics instead of binary logic, allowing for more powerful computations. He describes superposition and parallel universes as key concepts. Current advancements include algorithms for signal processing and potential applications in health monitoring. Neven emphasizes the importance of error correction and predicts significant future capabilities in medicine, energy, and understanding consciousness. Progress continues toward building a practical quantum computer.

Imagine a dinner-plate-sized chip that runs AI at unprecedented scale without racks of GPUs. Cerebras’ Wafer Scale Engine 3 delivers four trillion transistors and 900,000 cores on a single wafer. Feldman says the hard part of AI is the interchip communication, so the solution is to keep computation on one giant wafer instead of fragmenting across many devices. The result is faster training and lower power, supported by an integrated system for data handling, cooling, and networking. Over the past year Cerebras has deployed exaflop-scale AI compute with customers across North America, Europe, and the Middle East, including cloud partners. The approach contrasts with GPU clusters by removing the need for large-scale distributed compute; Nvidia’s Mellanox acquisition underscored the same problem. Cerebras’ technology has been applied to diverse challenges: predicting virus mutations with Argonne National Laboratory, analyzing epigenomic data with GlaxoSmithKline, and training an Arabic language model with G42 that powers regional services. They collaborate with Mayo Clinic and TotalEnergies on imaging, genomics, and reservoir modeling. Looking ahead, Feldman says the path is iterative: scale hardware, improve software utilization, and leverage sparsity to cut compute without losing accuracy. He envisions broader AI adoption in healthcare and industry, with sovereign clouds expanding access to massive AI compute. The hardware-software-data ecosystem will continue to evolve, and the company aims to be 10x better rather than marginally improved. Their focus on domain-specific efficiency—rather than chasing a single architecture—helps them adapt as models evolve, from transformers to new ideas. The pace is relentless.

In this episode of the Into the Impossible podcast, Brian Keating interviews Nobel laureates Duncan Haldane and Roger Penrose about Haldane's 2016 Nobel Prize for his work on topological matter and phase transitions. Haldane discusses the challenges of explaining quantum mechanics, emphasizing its mysterious nature and the excitement it generates among young physicists. He explains topology as a mathematical concept that describes properties of objects, using analogies like bagels and pretzels to illustrate his discoveries. Haldane reflects on the collaborative nature of scientific progress and the unpredictable benefits of fundamental research, suggesting that advancements in quantum technology could emerge from a deeper understanding of nature, despite their abstract origins.

At the frontier where physics meets artificial intelligence, Guillaume Verdon argues that the path to truly powerful AI runs through the laws of nature themselves. Trained as a theoretical physicist, he describes a pivot from chasing a single unifying equation to building machines that mimic nature’s complexity. He helped pioneer Quantum Deep Learning, exploring how quantum information theory could guide neural networks, and he worked on early quantum algorithms and TensorFlow Quantum as the field formed. The aim, he says, is to understand the universe by compressing its data into useful representations. That scientific thread informs his current ventures: Extropy, the ambition to create physics-based AI processors; IAK and the Beff Jos persona used to explore ideas openly; and the broader EAK movement advocating rapid acceleration of AI. He describes a dual mission: embed AI inside the physics of the world, and embed the world’s physics inside AI. In this worldview, civilization’s growth depends on self-organization, adaptability, and increasingly intelligent systems that use energy more efficiently. Kardashev-scale thinking anchors the long-term goal: more intelligence per watt across the cosmos. Technically, Verdon describes Theramic computing—an approach that uses stochastic electron dynamics in superconductors and silicon to run learning algorithms at high speed with far lower energy cost than today’s GPUs. The project treats information theory, thermodynamics, and machine learning as a single framework, where Monte Carlo-style sampling can be realized physically. Early hardware will be silicon and room-temperature, with superconducting platforms for research. The promise is to accelerate problem-specific tasks, then scale to foundational models that adapt to many applications. On regulation and societal impact, he argues against heavy-handed AI restrictions and for policy that remains flexible as technology evolves. He frames AI as an augmenting partner—an ongoing, iterative process rather than a fixed upgrade—and notes that fear can undermine progress. The strategy includes open collaboration, openness about algorithmic tradeoffs, and a belief that distributed competition will align AI with human values. He also reflects on his Twitter-era Beff persona as a way to seed optimistic, future-facing memes that keep the pace of change constructive.

Brian Keating welcomes John Preskill, a significant figure in his career, to discuss quantum computing and its implications for fundamental physics. Preskill defines a quantum computer as a device leveraging quantum mechanics to outperform classical computers in specific problem-solving scenarios, particularly in understanding quantum systems. He emphasizes the importance of exploring the "entanglement frontier," where quantum states become highly correlated, presenting opportunities for scientific discovery. The conversation touches on the Church-Turing thesis, which suggests that a universal computer can simulate any physical process. Preskill argues for a "quantum Church-Turing thesis," positing that quantum computers can efficiently simulate natural processes that classical computers cannot. He acknowledges the current limitations of quantum computing, stating that while it excels in certain areas like cryptography and simulating quantum physics, its full potential remains largely unexplored. Preskill addresses skepticism regarding quantum computers, asserting that they are not universally superior but can dramatically speed up solutions for specific structured problems. He highlights the potential for quantum computing to revolutionize fields such as material science and chemistry, although practical applications may still be decades away. The discussion also covers the concept of quantum supremacy, which Preskill describes as the ability of quantum computers to perform tasks that classical computers cannot do efficiently. He recounts Google's 2019 announcement of achieving quantum supremacy, where their quantum device completed a complex task faster than the best classical supercomputers could. Preskill reflects on the technological advancements that have enabled the manipulation of single quantum systems, which are crucial for quantum computing. He notes that while significant progress has been made, challenges remain, particularly in error correction and scaling up quantum systems. The conversation shifts to the philosophical implications of quantum mechanics and artificial intelligence. Preskill expresses optimism about AI's potential to contribute creatively to scientific discovery, suggesting that human cognition is not inherently magical and can be replicated in machines. As the discussion concludes, Preskill shares wisdom about maintaining a sense of humor, being open to learning from experiments, and the importance of objectivity in scientific inquiry. He emphasizes the need for collaboration between theorists and experimentalists to advance the field of quantum computing and physics as a whole.

PsiQuantum’s interview centers on the company’s audacious plan to scale quantum computing into a commercially impactful, million-qubit machine, financed by a near $2 billion round and guided by a philosophy of building a transformational, rather than incremental, technology. The guest emphasizes that typical progress in quantum research has been slow, and PsiQuantum chose to invest in the full stack required for a very large system—specializing in semiconductor manufacturing, networking, cooling, and related infrastructure—rather than staging a sequence of smaller, market-ready demos. The conversation situates this choice within a broader tech landscape where frontier companies like TSMC, ASML, SpaceX, Nvidia, and OpenAI succeed by pushing the limits of science and engineering on the frontier, often with government backing. A central theme is that value will come not from selling a single device but from delivering access to a machine that can generate fundamental knowledge about chemistry, materials, and processes that currently elude conventional computation. To realize this, PsiQuantum has pursued a manufacturing-centric roadmap, partnering with a Tier 1 foundry in the United States, GlobalFoundries, and building out large-scale sites in Australia and Chicago to house the core capabilities and helium-based cryogenics needed for their architecture. The interview also delves into governance and validation: government-backed diligence, DARPA’s red-team approach, and the scrutiny of major investors like BlackRock, Baillie Gifford, Temasek, and others who have backed the venture as it tiptoes toward a stage where practical commercial deployments might emerge. The host pressing a hard question about a trillion-dollar valuation prompts a clarifying point that the business model centers on delivering time on the machine to enterprise customers, while exploring deeper vertical integration and R&D ecosystems to turn breakthrough findings into scalable revenue streams. The dialogue also covers the nuanced relationship with industry peers, the evolving perception of quantum as an instrument rather than a conventional computer, and the ethical and geopolitical realities of pursuing such a transformative technology. In closing, the guest reflects on the pace of site construction, the scale of the South Bay facility, and the aspiration to turn a foundational scientific leap into a generational business that redefines how industries innovate at the molecular and atomic levels.

In this conversation, Lex Fridman speaks with Jeffrey Shainline, a scientist at NIST, about optoelectronic intelligence and the future of computing. Shainline explains that optoelectronic intelligence refers to a brain-inspired computing architecture that uses light for communication and superconducting electronics for computation. He contrasts this with traditional semiconducting electronics, discussing the fundamental principles of how computers work, particularly focusing on transistors and the role of silicon as a semiconductor. Shainline elaborates on the scaling of transistors, noting that the feature size has decreased significantly over the decades, enabling more computational power within the same chip size. He emphasizes the importance of manufacturing techniques like photolithography and ion implantation in achieving these advancements. The conversation also touches on the challenges of further miniaturization and the limits of silicon technology. The discussion shifts to superconducting electronics, where Shainline describes how superconductors can carry current without dissipation at low temperatures, leading to faster and more energy-efficient computing. He introduces the concept of Josephson junctions, which are crucial components in superconducting circuits, and explains their potential for high-speed operations compared to traditional transistors. Fridman and Shainline explore the implications of neuromorphic computing, which aims to mimic the brain's architecture and processing capabilities. Shainline highlights the need for communication networks that can efficiently transmit information, suggesting that light could be a more effective medium than electrons for certain applications. He discusses the potential for integrating light sources with superconducting electronics, which could lead to novel computing architectures. The conversation delves into the philosophical implications of technology and intelligence, with Shainline proposing that the universe's physical parameters may have evolved to facilitate technological innovation. He references Lee Smolin's idea of cosmological natural selection, suggesting that intelligent civilizations could emerge as a byproduct of the universe's evolution. Fridman and Shainline also discuss the rarity of intelligent life in the universe, considering the conditions necessary for life and technology to develop. They ponder the future of computing, particularly in relation to machine learning and the potential for superconducting systems to outperform traditional silicon-based technologies. Ultimately, the dialogue emphasizes the intersection of physics, engineering, and philosophy in understanding the universe and our place within it, while also exploring the possibilities of future technologies that could reshape our understanding of intelligence and computation.

Lawrence Krauss welcomes John Preskill, a prominent physicist and director of the Institute for Quantum Information and Matter at Caltech, to the Origins Podcast. They discuss Preskill's journey from fundamental particle physics and cosmology to quantum computing, a field he has significantly influenced. Preskill recalls his early interest in physics sparked by the space program and influential teachers at Princeton, including Val Fitch and John Wheeler. The conversation shifts to the hype surrounding quantum computing, with Krauss emphasizing the need to distinguish between reality and exaggeration. Preskill explains that quantum computers leverage the principles of quantum mechanics, particularly superposition and entanglement, to perform calculations that classical computers struggle with. He highlights the challenges of decoherence, where quantum systems interact with their environment, leading to errors in computations. They discuss various hardware approaches for quantum computing, including trapped ions and superconducting circuits. Trapped ions use electromagnetic fields to manipulate individual atoms, while superconducting circuits operate at low temperatures and utilize Josephson junctions to create qubits. Both technologies face challenges related to error rates in quantum gates, which must be minimized for reliable computations. Preskill introduces the concept of NISQ (noisy intermediate-scale quantum) devices, which are currently available but not yet capable of solving complex problems without significant error correction. He emphasizes the importance of quantum error correction, which encodes information in a way that protects it from environmental noise, allowing for more reliable computations. The discussion touches on the potential applications of quantum computing in fields like chemistry and materials science, as well as the need for new cryptographic systems to protect against future quantum threats. Preskill expresses excitement about the future of quantum computing, particularly its potential to deepen our understanding of quantum gravity and the nature of space itself. In closing, Krauss and Preskill reflect on the poetic nature of their discussions, highlighting the profound questions that quantum computing may help answer about the universe. Preskill's insights and experiences as a physicist underscore the ongoing journey of discovery in this rapidly evolving field.

In this a16z podcast, Chad Rigetti, CEO of Rigetti Computing, discusses the evolution and potential of quantum computing with Chris Dixon. They explore the limitations of classical computing, particularly as Moore's Law approaches its physical limits, leading to challenges in energy efficiency and manufacturing costs. Quantum computing, rooted in quantum mechanics, offers a new paradigm by encoding information in quantum states, allowing for exponential growth in computational power with each additional qubit. Rigetti highlights two primary applications for quantum computing: simulating quantum systems in computational chemistry and solving complex optimization problems relevant to machine learning. The conversation emphasizes the need for sophisticated classical computers to complement quantum systems, enabling hybrid algorithms that leverage both technologies effectively. The quantum computing field has grown significantly, with thousands of researchers globally, including efforts from major companies like IBM and Google. Rigetti aims to build a full-stack quantum computing platform, integrating hardware and software to facilitate access to quantum capabilities. While concerns exist about quantum computers potentially breaking current cryptographic systems, Rigetti believes the most exciting applications lie in advancing artificial intelligence and revolutionizing healthcare and energy solutions.

The conversation centers on the trajectory of quantum computing, tracing how the field has shifted from university labs to ambitious startup efforts. Ashley Vance reflects on the evolution from early, theoretical experiments to the current reality where multiple groups are attempting to scale qubits, chip by chip, and to integrate software techniques for error correction. The hosts contrast the original hype of quantum computing with practical milestones, emphasizing that dramatic progress has occurred, but the path to a useful machine remains complex, expensive, and highly collaborative among researchers, engineers, and funders. The discussion highlights Scantum (PsiQuantum) as aiming for a milestone that would differentiate it from peers, while also acknowledging the broader challenge of choosing a single architectural approach in a field crowded with competing qubit technologies. The guests offer a window into the startup mindset in deep tech: the necessity of a singular, audacious goal, the difficulty of turning academic rigor into a manufacturable product, and the importance of visible progress and credibility. The human element of building such a company—leadership, team alignment, and the balance between engineering perfection and product practicality—receives detailed attention, including reflections on Apollo-era motivation and the patience required to endure long development cycles in hardware deep tech.

Quantum computers can solve problems that classical computers cannot, such as modeling complex molecules and breaking encryption. They use quantum bits (qubits) that exist in superposition, allowing simultaneous computations. Qubits can be made from particles like electrons or atoms, and their states are linked through quantum entanglement. However, challenges remain, including maintaining qubits in a stable quantum state. Current designs include superconductors and quantum dots. While progress is being made, meaningful quantum computers are still decades away, with expectations likely to fluctuate during this period.

In this conversation, Lex Fridman speaks with Scott Aaronson, a professor at UT Austin and director of its quantum information center, focusing on quantum computing and its philosophical implications. Aaronson emphasizes the importance of philosophy in technical fields, arguing that it helps frame and understand complex questions, such as the nature of consciousness and free will. He discusses the historical context of computer science and philosophy, referencing Alan Turing's engagement with philosophical questions and the relevance of formal systems in practical applications. Aaronson introduces quantum computing as a new computational paradigm based on quantum mechanics principles, explaining concepts like qubits, superposition, and interference. He clarifies that quantum computers exploit these phenomena to solve problems faster than classical computers, although they do not operate in a magical realm outside traditional computation. The discussion touches on quantum supremacy, a milestone achieved by Google, which demonstrates a quantum computer performing a task faster than classical computers, though not necessarily useful yet. The conversation also addresses the challenges of building scalable quantum computers, particularly noise and decoherence, and the need for error correction. Aaronson highlights the potential applications of quantum computing in simulating quantum systems, which could revolutionize fields like chemistry and materials science. He cautions against overhyped claims in the quantum computing space, emphasizing the need for rigorous evidence of speed-ups over classical algorithms. Ultimately, the dialogue reflects on the intersection of science, philosophy, and the future of technology.

In this a16z podcast, Jeff Cordova and Vijay Pande discuss the evolution and potential of quantum computing. They emphasize the need to rethink algorithms for different architectures, such as GPUs and quantum computers, highlighting that quantum computing operates on probabilistic principles rather than deterministic logic. The conversation touches on the significance of hybrid computing, where classical and quantum systems interact, and the necessity of cloud access for quantum resources due to their complex operational requirements. They note that while quantum computing is still developing, it has the potential to solve problems beyond the reach of classical computers, particularly in fields like computational chemistry. The discussion concludes with the idea that the true capabilities of quantum computers remain largely unexplored, presenting both challenges and opportunities for future innovation.

In a coin game played on a quantum computer, the quantum system won almost every time due to its ability to harness superposition and uncertainty. Quantum computers operate differently from regular computers, allowing for potential applications in secure encryption, drug development, and information teleportation. These advancements could significantly impact security, healthcare, and communication in the future.

AI plans collide with hardware bets as Possible explores a future where wearables and AI converge. Humane, the AI pen, shut down and was acquired by HP for 116 million after funding. The discussion moves to wearables in professional settings: glasses that parse the world, assist with tasks, or help blind users locate doors. Initial adoption may be in nursing, medicine, firefighting, and public service, where augmented perception improves outcomes. Investors favor software, but some teams pursue quantum-powered acceleration for atom-scale problems, suggesting consumer gains will follow deeper, context-aware use rather than flashy hardware. Majira One, a Microsoft-backed quantum chip, signals a shift: quantum computing could accelerate drug discovery and materials research when enough logical cubits exist. Opinions differ on immediacy; 2,000 to 5,000 logical cubits are cited for meaningful security breakthroughs, and consumer benefits may still be years away. The UK is probing copyright rules for AI training, while Ishiguro frames the debate as a fork in the road for creators, balancing protection with innovation. Deep research tools promise speed but require verification, and Parth Patil says their best use is exploratory synthesis rather than definitive facts. Vibe coding and co-pilots suggest a future where software is built by describing needs, with natural languages becoming the interface while humans review results. The episode closes by highlighting AI's potential to enable faster, more collaborative creativity, despite pains.