In a quantified world, the act of creation remains mysterious. Where do ideas come from? How does an artist translate a concept or a feeling into the final work that we get to read or view? The interior drama of that mystery becomes ever more visible as the singular artist expands into a collaboration. How do relationships change the trajectory and originality of creativity? Few novels have better distilled the essence of these questions than Tomorrow, and Tomorrow, and Tomorrow by Gabrielle Zevin, which chronicles the multi-decade collaboration between two video game designers as they mature from grade school into the limelight of a cutthroat industry on the cusp of popular success. Inventive, heartfelt, and sophisticated, the novel was a breakout hit and was selected as Amazon’s book of the year for 2022. This week on “Securities”, host Danny Crichton joins up with novelist Eliot Peper and Lux’s own scientist-in-residence Sam Arbesman to talk about the messages that the novel offers our own creative lives. We talk about the building of virtual worlds, the hero’s journey of creation, the uniqueness versus repetitiveness of producing art, whether video games are entering the literary zeitgeist, why the book garnered such popular success and finally, narratives of individuals versus groups.

We perceive the world through our senses, watching the sunset, hearing the staccato of a violin soloist, smelling and ultimately tasting the chocolate and butter of freshly-baked cookies, and of course, feeling the touch of a loving partner. Yet while scientists have answered fundamental questions about color and audio, from understanding their physics to constructing mathematical representations of them, there remains a huge gap when it comes to smell. Given how much more complex and higher dimensional it is, smell is an extraordinarily hard sense to capture, a problem which sits at the open frontiers of neuroscience and information theory. Now after many decades of discovery, the tooling and understanding has finally developed to begin to map, analyze and ultimately transmit smell. Joining “Securities” host Danny Crichton is Alex Wiltschko, CEO and founder of Osmo, a Lux-backed company organized to give computers a sense of smell. He’s dedicated his life (from collecting and smelling bottles of perfume in grade school to his neuroscience PhD) to understanding this critical human sense and progressing the future of the field. In this episode, we talk about smell and memory, the history of sense science, the mathematical challenges of modeling scent, the human physiology of smell and our surprising performance against even the best scientific lab equipment, the importance of chemical sensing, creating the digital olfaction group at Google Brain, how the mixture modeling problem remains the last and key frontier of this science, and finally, why the declining power of insect repellant is an important climate change challenge that the new science of smell can potentially solve.

It’s been a long road to mastering the cell, but biological scientists think they are getting closer and closer to understanding the fundamental mechanics of the kernels of life that make up our bodies. Decades after the sequencing of the first human genome, we now have a much more comprehensive understanding of how to discover a cell’s functions — and increasingly, the tools to actually analyze and prove that our models and theories about them are correct. That’s been the domain of single-cell analysis and a novel technique in genetic science, which has been dubbed “perturbation biology”: making extremely small changes to the genetic code inside of cells and then observing how that cell’s functions change. What began with 18 cells and limited observational data in a single lab has now grown exponentially to hundreds of thousands of cells and millions of observations globally. That massive increase in data has forced the creation of a whole new set of analytical tools to process this data and derive foundational insights into the workings of cells. How do all of these new laboratory experiments work and what kind of software tools are needed to progress the most advanced theories today? Joining host Danny Crichton on “Securities” this episode is Rahul Satija, an associate professor at New York University and a core member of the New York Genome Center as well as Lux’s own Shaq Vayda. We’ll talk about how biological tools like CRISPR power perturbation bio, why scientists are increasingly moving away from indirect experiments to direct experiments and what that means for the future of the field, how we comprehend cell heterogeneity, if we’re getting closer to “fundamental truth” in biology, and finally, why theoretical molecular scientists are increasingly going to need large-scale clinical trials for the next-generation of health treatments.

ChatGPT has overtaken the cultural zeitgeist faster than any consumer service in the history of technology, with some analysts estimating that it has already been used by more than 100 million people. So when OpenAI, ChatGPT’s creator, live-streamed the launch of its new AI model GPT-4, there was a rush of excitement reminiscent of the Apple product launches of the past. It’s been about 24 hours since GPT-4’s public launch, and all of us here at Lux have already extensively played around with it, so it seemed apt for a rapid response “Securities” episode on our very first impressions. Joining host Danny Crichton is Lux Capital partner Grace Isford, who not only has been playing around with ChatGPT, but also Anthropic’s new bot Claude, which was a bit overshadowed between SVB’s situation and OpenAI’s announcement. We talk about GPT-4 and what’s new, its new frontiers of performance, the increasingly impenetrable black box OpenAI is establishing around its company and processes, the company’s competitive dynamics with big tech, and much more.

The Lux LP quarterly letter has become an institution for its intricate weave of pragmatic cynicism about human nature and unbounded optimism about the power of human progress in the face of macroeconomic forces. We released the latest quarterly letter on the theme of “From Strife to Strive” just before the collapse of Silicon Valley Bank last week. With more strife than ever in the market, where will entrepreneurs strive next? Joining me (Danny Crichton) to talk about our analysis of what’s coming in 2023 is our own Josh Wolfe, who predicts that Xi Jinping now plays a much larger oracular role for the American economy than even Warren Buffett. China’s competition with the U.S. is forcing venture investors and political leaders to reallocate capital much more aggressively toward the hard sciences — portending important advances ahead. We also talk about open cultures, reconsideration of established truths and loss aversion, the online furor over induction stoves, Lux’s concept of “inner space, outer space and latent space”, the future of ChatGPT and the rise of what Josh dubs “Chatphishing”, the potential terrorism of 21st century Luddites, and finally, macro dynamics and why the chaos of the next two years will lay the foundation for the entrepreneurial striving in the decade ahead.

While much of the venture world has hit a reset in 2023, you’d never know that in artificial intelligence, where fire marshals are shutting down crammed engineering meetups and startups are once again raising at eye-watering valuations. Why the excitement? Because for founders, technologists and VCs, it feels like the everlasting promise of AI dating back to the 1950s and 1960s is finally on the cusp of being realized with the training and deployment of large language models like GPT-3. To hear about what’s happening on the frontlines of this frenetic field, Lux Capital partner Grace Isford joins “Securities” host Danny Crichton to talk about what she’s seeing in 2023 across the AI tech landscape. We talk about her impressions at the recent AI Film Festival in New York City hosted by Lux’s portfolio company Runway, how developers are being empowered with new technologies in Python and TypeScript and why that matters, and finally, how the big tech giants like Microsoft, Google and Amazon are carefully playing their cards in the ferocious competition to lead the next generation of AI cloud infrastructure.

Artificial intelligence has become ambient in our daily lives, scooting us from place to place with turn-by-turn navigation, assisting us with reminders and alarms, and guiding professionals from lawyers and doctors to reaching the best possible decisions with the data they have on hand. Domain-specific AI has also mastered everything from games like Chess and Go to the complicated science of protein folding. Since the debut of ChatGPT in November by OpenAI however, we have seen a volcanic interest in what generative AI can do across text, audio and video. Within just a few weeks, ChatGPT reached 100 million users — arguably the fastest ever for a new product. What are its capabilities and perhaps most importantly given the feverish excitement of this new technology, what are its limitations? We turn to a stalwart of AI criticism, Gary Marcus, to explore more. Marcus is professor emeritus of psychology and neural science at New York University and the founder of machine learning startup Geometric Intelligence, which sold to Uber in 2016. He has been a fervent contrarian on many aspects of our current AI craze, the topic at the heart of his most recent book, Rebooting AI. Unlike most modern AI specialists, he is less enthusiastic about the statistical methods that underlie approaches like deep learning and is instead a forceful advocate for returning — at least partially — to the symbolic methods that the AI field has traditionally explored. In today’s episode of “Securities”, we’re going to talk about the challenges of truth and veracity in the context of fake content driven by tools like Galactica; pose the first ChatGPT written question to Marcus; talk about how much we can rely on AI generated answers; discuss the future of artificial general intelligence; and finally, understand why Marcus thinks AI is not going to be a universal solvent for all human problems.

They say that you miss 100% of the shots you don’t take, but what if each shot costs money and is actually a tradeoff with taking a different shot? Time and money are limited, and that means we must constantly balance investing in our current projects and ideas against seeking out new opportunities. While there has been prodigious work published on how to find the “next big thing”, few researchers have investigated what it takes to just throw in the towel, jump ship, fold and quit in the face of a bad situation. Joining us on “Securities” today is Annie Duke, a World Series of Poker champion who researches cognitive psychology at the University of Pennsylvania. She recently published her new book “Quit: The Power of Knowing When to Walk Away,” which explores the nature of quitting, the cognitive challenges in confronting loss, and the tactics required to identify when to quit — and how to do so. In conversation with Lux Capital’s own Josh Wolfe, the two discuss the challenges of walking away, why professional poker players are better at quitting than amateurs, the geopolitics of war, and the importance as always of premortems for quitting.

Technology’s prime and still growing role in society has led to a crescendo of criticism that it has exacerbated inequality. Critics say that the economic models and algorithms underpinning out apps and platforms are tearing apart our social fabric, fracturing the economy, casualizing labor, and increasing hostility between nations. But for all the negativity around technology, there is a parallel positive story of how technology can empower people to achieve their best lives. Whether it’s dynamically adjusting insulin pumps that allow diabetics greater freedom to pursue their dreams, or reliable algorithms that can reduce human bias in everything from hiring to dating, technology has also added tremendous value to society. That’s the theme of “The Equality Machine: Harnessing Digital Technology for a Brighter, More Inclusive Future,” a new book by Orly Lobel, the Warren Distinguished Professor of Law and Director of the Center for Employment and Labor Policy at the University of San Diego. Lobel joins host Danny Crichton to talk about how her daughter became bionic, why alarmist titles of recent critical tech books belie the comparative advantage of algorithms, the actual black box of human minds, feedback loops in doctor’s offices and the medical professions, and finally … sex robots. Because they have feelings (and algorithms) too.

No technology has as many dual-use challenges as artificial intelligence. The same AI models that invent vivacious illustrations and visual effects for movies are the exact models that can generate democracy-killing algorithmic propaganda. Code may well be code, but more and more AI leaders are considering how to balance the desire for openness with the need for responsible innovation. One of those leading companies is Hugging Face (a Lux portfolio company), and part of the weight of AI’s safe future lies there with Carlos Muñoz Ferrandis, a Spanish lawyer and PhD researcher at the Max Planck Institute for Innovation and Competition (Munich). Ferrandis is co-lead of the Legal & Ethical Working Group at BigScience and the AI counsel for Hugging Face. He’s been working on Open & Responsible AI licenses (“OpenRAIL”) that fuse the freedom of traditional open-source licenses with the responsible usage that AI leaders wish to see emerge from the community. In today’s episode, Ferrandis joins host Danny Crichton to talk about why code and models require different types of licenses, balancing openness with responsibility, how to keep the community adaptive even as AI models are added to more applications, how these new AI licenses are enforced, and what happens when AI models get ever cheaper to train.