The Generalist

For decades, drug discovery has shifted away from nature and toward biology-first approaches. Viswa Colluru believes that shift was a catastrophic mistake. His company, Enveda Biosciences, has raised over $500 million to build a “search engine for nature’s chemistry.” The mission is personal: he grew up around his father’s pharmacy in India and later lost his mother to a treatable cancer whose medicine his family couldn’t afford. Many life-changing medicines, including morphine, aspirin, and metformin, originated in nature, but there has never been a reliable, scalable way to systematically explore its chemistry. Colluru founded Enveda in 2019 with $55,000 of his own savings to change that. The company has since identified 18 drug candidates, with three now in clinical trials.

In our conversation, we explore:
Why the pharmaceutical industry abandoned nature (and why that was a massive mistake)
How Enveda built a system to decode unknown molecules in nature
The deeply personal story of his mother’s battle with leukemia and how it shaped his life’s work
Why old ideas, from immunotherapy to natural products, often hold the most latent potential
How Enveda developed 18 drug candidates for about $1 million each instead of $10-15 million
Enveda’s three leading drug candidates targeting eczema, obesity, and ulcerative colitis
Why first-in-class medicines capture the vast majority of returns in pharma
What competitive table tennis taught him about building companies
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Timestamps
(00:00) Introduction to Viswa Colluru
(03:57) His father’s pharmacy and early exposure to Western and Ayurvedic medicine
(07:06) Early pull toward technology
(09:29) His mother’s leukemia diagnosis
(14:24) Studying Biotechnology
(16:07) Graduate school
(17:55) Studying immunotherapy when it was unfashionable
(24:23) Innovation vs. novelty
(27:24) Lessons from table tennis
(32:05) Joining Recursion
(37:10) Learning urgency and courage
(40:42) What launched Enveda
(45:40) The limits of reductionist drug discovery
(49:53) Chemistry-first approach
(52:17) Raising $225K and investing $55K personally
(56:04) Initial studies and targets
(1:04:30) Three categories of leading drugs: Eczema, obesity, ulcerative colitis
(1:13:27) Why GLP-1s are not the whole answer
(1:18:27) Enveda’s long-term vision
(1:21:31) Book recommendation
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Follow Viswa Colluru
LinkedIn: https://www.linkedin.com/in/viswacolluru
X: https://x.com/viswacolluru
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Resources and episode mentions: https://www.generalist.com/p/the-future-of-drug-discovery
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Production and marketing by penname.co. For inquiries about sponsoring the podcast, email [email protected].

Tudor Achim is the co-founder and CEO of Harmonic, a startup working to solve one of AI’s hardest problems: mathematical reasoning. In July 2024, Harmonic achieved gold-medal-level performance on International Math Olympiad problems alongside systems from OpenAI and Google DeepMind—but with a key difference: every proof Harmonic submitted was formally verified. Tudor's path to Harmonic wound through competitive piano, computational biology, and autonomous driving. He studied at Carnegie Mellon's music preparatory school, worked on machine learning at Quora, briefly pursued a PhD before dropping out, and then co-founded an autonomous driving company, Helm.ai. Harmonic's core product, Aristotle, uses reinforcement learning and the programming language Lean 4 to solve problems and verify solutions.

In our conversation, we explore:
Why Tudor believes math is the fundamental toolkit to understand the world
How Harmonic uses hallucinations as a feature, not a bug
How Aristotle works and the applications beyond pure mathematics
The reinforcement learning process that lets Harmonic generate synthetic training data and solve problems humans have never attempted
Why Tudor believes AI could surpass human mathematicians on specific tasks within 2–3 years
Why the future of mathematics looks more like GitHub than academic journals
The alternating pattern between intellect leaps and data leaps throughout scientific history
How studying piano under an extraordinary teacher taught Tudor discipline and the value of sticking with hard problems
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Thank you to the partners who make this possible
Brex: The intelligent finance platform.
Guru: The AI source of truth for work.
Rippling: Stop wasting time on admin tasks, build your startup faster.
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Transcript: https://www.generalist.com/p/how-a-20-person-startup-won-gold
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Timestamps
(00:00) Intro
(03:34) From competitive piano to computer science
(06:28) The mathematical foundations of music (and why Tudor keeps them separate)
(08:24) Can AI ever create art with true intent?
(09:51) Early obsessions
(12:52) Defining intelligence
(14:49) Discovering machine learning’s potential at Quora
(17:30) Why Tudor chose computational biology for his PhD
(19:19) The decision to drop out and build Helm.ai
(22:55) The two breakthroughs that made mathematical AI possible in 2023
(25:28) The importance of Lean 4
(28:21) How Tudor and Vlad Tenev discovered they shared the same impossible dream
(32:35) Why formal verification became the core conviction
(34:21) The timeline for AI surpassing human mathematicians
(35:25) An overview of Aristotle: the world’s first always-correct mathematical agent
(38:12) Why Tudor says hallucinations are the engine of creativity
(39:30) The translation challenge from natural language to formal proof
(40:40) Reinforcement learning
(42:10) Why Aristotle is both faster and cheaper than alternatives
(43:34) Tradeoffs and use cases
(45:34) Math in AI now and what’s next
(47:38) Tying with OpenAI and DeepMind at the International Math Olympiad
(49:08) Democratizing AI and correctness
(53:13) Tudor’s 2030 thesis
(56:02) History’s alternating rhythm of thinking and measuring
(57:53) What Tudor has been wrong about
(58:52) What Tudor’s best at
(1:00:18) Final meditations
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Follow Tudor Achim
LinkedIn: https://www.linkedin.com/in/tudorachim
X: https://x.com/tachim/with_replies
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Resources and episode mentions: https://www.generalist.com/p/how-a-20-person-startup-won-gold⁠
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Production and marketing by penname.co. For inquiries about sponsoring the podcast, email [email protected].

Anil Varanasi, co-founder and CEO of Meter, is building a new kind of networking company for the AI era. Alongside his brother Sunil, he has helped raise more than $250 million to challenge incumbents like Cisco with a vertically integrated approach spanning hardware, software, deployment, and ongoing operations, all delivered through a utility-style model. His view is that networking has remained largely unchanged for decades, even as it has become foundational to everything from AI workloads to real-world infrastructure. Meter’s ambition is not just to improve existing networks, but to make them autonomous over time. Before starting the company, Anil and Sunil were deeply involved in filmmaking, a background that still shapes their philosophy of building with cathedral-level craft across every layer of the stack.

Together we explore:
The “burden of knowledge” and why progress is getting harder across fields
Why most companies over-index on technology and ignore business model innovation
The three ways companies create advantage: technology, delivery, and business model
How Meter’s trade-in model borrows from the automotive industry
Why networking should function like electricity or water—not hardware
Lessons from Japanese vending machine logistics for infrastructure deployment
The hidden coordination problem behind vertically integrated companies
Why Anil believes “common knowledge” is often wrong
How COVID forced Meter to abandon geographic constraints and scale nationally
The case for fully autonomous networks in a world of exploding demand
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Thank you to the partners who make this possible
.tech domains: An identity for builders at their core.
Granola: The app that might actually make you love meetings.
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Transcript: https://www.generalist.com/p/the-case-for-autonomous-networks
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Timestamps
(00:00) Introduction to Anil Varanasi and Meter
(03:52) The burden of knowledge and slowing innovation
(08:18) Losing creativity vs gaining expertise
(10:25) What Meter actually does
(13:26) Early life, immigration, and upbringing
(15:47) Parental influence
(20:03) Film, storytelling, and creative influence
(22:55) Why Anil didn’t pursue filmmaking
(25:44) Parallels between company building and filmmaking
(27:00) Early programming and building
(28:05) George Mason and understanding systems
(29:59) The dynamic of working with his brother as a co-founder
(34:03) His first business and lessons learned (or lack thereof)
(35:15) Lessons from successful companies
(38:16) Japanese vending machines and logistics insight
(41:10) Scrapping 18 months of work
(42:40) Conviction and long-term company building
(46:02) COVID shock and near-death moment
(49:59) Building hardware like a cathedral
(52:25) Rethinking the networking business model
(57:06) Build vs buy and transaction costs
(59:39) Networking as infrastructure and utility
(01:01:30) The case for autonomous networks
(01:03:25) Hiring, talent, and what actually matters
(01:06:15) Big unanswered questions (sleep, science)
(01:07:28) Rethinking education
(01:09:02) Infinite games and long-term thinking
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Follow Anil Varanasi
LinkedIn: https://www.linkedin.com/in/anilcv
X: https://x.com/acv
Website: https://anilv.com
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Resources and episode mentions: https://www.generalist.com/p/the-case-for-autonomous-networks⁠
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Production and marketing by penname.co. For inquiries about sponsoring the podcast, email [email protected].

Much of the fear around AI centers on misalignment – the idea that powerful systems might act against human interests. Vincent Weisser worries about something different: what happens if advanced AI systems are perfectly aligned with the interests of a small group of institutions? That concern led him to co-found Prime Intellect, a startup building open infrastructure for training and deploying advanced AI models. Before Prime Intellect, Weisser helped organize Vitalik Buterin’s Zuzalu experiment and worked in decentralized science, where he helped unlock roughly $40 million in funding for unconventional research. Today, he’s applying that same open ethos to AI, working to ensure the tools that shape superintelligence remain broadly accessible rather than concentrated in the hands of a few.
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In our conversation, we explore:
Why Vincent believes multiple superintelligences are safer than one
The intellectual influences that shaped Vincent’s thinking about intelligence and progress, including David Deutsch and Nick Bostrom
Prime Intellect’s evolution from distributed compute infrastructure to frontier model training and reinforcement learning tools
Why Vincent believes open and decentralized science could accelerate discovery
The Zuzalu experiment and what it suggests about the future of scientific communities
The role of aesthetics and craft in building technology
Why Europe might have a cultural advantage in a post-superintelligence world
Vincent’s predictions for the next five years of AI
—
Thank you to the partners who make this possible
Granola: The app that might actually make you love meetings.
Brex: The intelligent finance platform.
Rippling: Stop wasting time on admin tasks, build your startup faster.
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Transcript: https://www.generalist.com/p/why-one-superintelligence-is-more
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Timestamps
(00:00) Introduction to Vincent Weisser
(03:28) The book behind Prime Intellect’s name
(07:35) The case for suffering
(09:35) An overview of Prime Intellect
(13:03) Why open source models matter
(21:18) Vincent’s intellectual influences
(25:17) Early years in the startup scene
(31:48) Funding science outside traditional institutions
(41:22) The past 6 months of AI progress
(43:45) Deciding to build Prime Intellect
(46:55) Why GPUs were the right starting point
(51:39) Training models on Prime Intellect
(59:48) Why beauty matters
(1:03:48) The Zuzalu experiment
(1:06:27) Prime Intellect’s AGI Easter egg
(1:11:13) Predictions for the next five years
(1:15:09) Final meditations
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Follow Vincent Weisser
LinkedIn: https://linkedin.com/in/vincentweisser
X: https://x.com/vincentweisser
Goodreads: https://www.goodreads.com/user/show/69248416-vincent-weisser
Website: https://primeintellect.ai
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Resources and episode mentions: https://www.generalist.com/p/why-one-superintelligence-is-more⁠
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Production and marketing by penname.co. For inquiries about sponsoring the podcast, email [email protected].

Karol Hausman is the co-founder and CEO of Physical Intelligence, a robotics company building a general-purpose “AI brain for the physical world.” The company has raised more than $1 billion in funding to develop foundation models that allow robots to operate across many machines, environments, and tasks rather than being programmed for a single purpose. The core thesis: the same scaling dynamics that transformed language models may also unlock robotic intelligence. But only if you resist every commercial pressure pushing you toward specialization. The central challenge isn’t mechanical design. It’s intelligence: how robots learn, generalize, and interact with a physical world that is far harder to simulate than it is to describe. Before launching Physical Intelligence, Karol worked at Google Brain and Stanford University, studying robot learning alongside researchers Sergey Levine and Chelsea Finn, who later became his co-founders.

In our conversation, we explore:
How growing up in a small town in Poland and watching Star Wars sparked Karol’s fascination with robots
The moment a lecture from Sergey Levine convinced him to abandon his PhD research direction and pivot fully to deep learning
Why robotics has historically lagged behind breakthroughs in language models
The case for building a general “AI brain” for the physical world rather than a single specialized robot
The role of real-world data in training robots, the limits of simulation, and how deployment could create a powerful data flywheel
The return of reinforcement learning and the parallels between human learning and robot training
The unique challenges of physical intelligence and why robots must operate with far higher reliability than language models
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Thank you to the partners who make this possible
Brex: The intelligent finance platform.
Granola: The app that might actually make you love meetings.
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Transcript: https://www.generalist.com/p/karol-hausman-physical-intelligence
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Timestamps
(00:00) Intro
(04:05) Karol’s early fascination with robots
(07:38) How Karol relates to Fei-Fei Li’s biography
(08:52) What inspired Karol to build better robots
(11:19) Philosophical influences
(15:33) Parallels between The Inner Game of Tennis and robotics
(18:21) Karol’s entry point to robotics and PhD program
(25:49) Combining robotics with LLMs: The Taylor Swift demo
(30:48) The 1970s SHRDLU AI experiment
(32:33) Founding Physical Intelligence
(35:13) How Lachy Groom got involved
(39:40) How research shapes what Physical Intelligence builds
(45:22) The importance of real-world data
(49:07) The return of reinforcement learning in robotics
(53:31) The risk of commercializing too early
(55:47) Finding the right partners for the business
(57:13) Open research questions
(1:00:00) NVIDIA’s simulation engines
(1:01:57) The surprising speed of progress
(1:04:16) Reliability in robotics
(1:07:31) Compensating for missing senses
(1:12:28) Book recommendation
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Follow Karol Hausman
LinkedIn: https://www.linkedin.com/in/karolhausman
X: https://x.com/hausman_k
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Resources and episode mentions: https://www.generalist.com/p/karol-hausman-physical-intelligence
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Production and marketing by penname.co. For inquiries about sponsoring the podcast, email [email protected].