<![CDATA[The Generalist]]>https://www.generalist.comhttps://substackcdn.com/image/fetch/$s_!hCZr!,w_256,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F2dfe63cd-67ea-49ba-92df-930520a822a0_500x500.pngThe Generalisthttps://www.generalist.comSubstackWed, 03 Jun 2026 15:17:53 GMT<![CDATA[What America Is Missing Between Sanctions and Nuclear War (Bryon Hargis, Co-Founder & CEO of Castelion)]]>https://www.generalist.com/p/what-america-is-missing-between-sanctionshttps://www.generalist.com/p/what-america-is-missing-between-sanctionsTue, 02 Jun 2026 12:04:31 GMT

“Warfare is always just adapting to whatever the other side is doing. And the person that wins is whoever adapts faster. And at this point in time, American aerospace is uniquely poorly suited to do that.” —Bryon Hargis, Co-Founder & CEO of Castelion

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Bryon Hargis is the co-founder and CEO of Castelion, a defense startup building low-cost hypersonic missiles designed to be manufactured at scale. Before founding Castelion, Bryon spent more than a decade at Johns Hopkins Applied Physics Laboratory and nearly six years at SpaceX, where he worked on national security space programs and saw firsthand how iterative engineering and manufacturing speed could reshape aerospace. Castelion’s first missile, Blackbeard, is slated for integration on the Navy’s F/A-18 Super Hornet in roughly a year.

In our conversation, we explore:

  • Why Bryon believes building missiles is paradoxically essential to maintaining peace

  • The game theory behind warfare and why tit-for-tat strategies require credible middle-ground responses

  • How China’s 2021 hypersonic test revealed not just a capability gap but a manufacturing and cost advantage

  • Why traditional aerospace processes—optimized for low risk and high cost—can’t compete with rapid iteration

  • What Bryon learned in his first week at SpaceX (after 12 years in traditional aerospace)

  • Why building a carrier-based, air-launched hypersonic missile as a first product was the hard but right choice

  • How focusing on manufacturability and cost over maximum capability can produce more effective deterrence

  • Why the person who adapts faster in warfare always wins, and how that shapes Castelion’s philosophy

Thank you to the partners who make this possible

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Timestamps

(00:00) Intro

(04:01) Why America needs hypersonic missiles

(07:13) China’s edge in hypersonics

(12:05) The missing middle ground in deterrence

(18:05) Preventing warhead ambiguity

(19:40) How hypersonics differ from ballistic missiles

(25:05) The economics of defensive vs. offensive systems

(28:21) How SpaceX differs from traditional aerospace

(37:40) Why Bryon chose to build in defense over space

(42:42) Key factors that drove Castelion’s success

(48:28) Designing Blackbeard, Castelion’s first hypersonic missile

(1:01:06) The importance of lower costs and quicker manufacturing

(1:10:04) Book recommendations

Follow Bryon Hargis

LinkedIn: https://www.linkedin.com/in/hargsb

X: https://x.com/hargsb

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]]><![CDATA[“Our Goal Is to Build an Electrical Engineer.” (Davide Asnaghi, Co-Founder & CEO of Diode)]]>https://www.generalist.com/p/our-goal-is-to-build-an-electrical-engineerhttps://www.generalist.com/p/our-goal-is-to-build-an-electrical-engineerTue, 19 May 2026 12:01:48 GMT

"China can do it because the labor is cheap. That's not true anymore. They are incredible at automating things and automating them in a way that makes them cost competitive." —Davide Asnaghi

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Davide Asnaghi is the co-founder and CEO of Diode, a Brooklyn-based startup using AI to design and manufacture circuit boards in the United States.

Before Diode, Davide worked on Apple’s Special Projects Group and spent time in Hong Kong and Shenzhen studying Asia’s electronics manufacturing ecosystem. That experience convinced him that PCB design, despite powering everything from smartphones and satellites to medical devices and autonomous systems, remained one of the most overlooked layers of the tech stack.

Since its founding just two years ago, Diode has landed Physical Intelligence and Saronic as customers and partnered with Anthropic to help Claude become a better electrical engineer. The company’s ultimate ambition: to make hardware as nimble as software.

In our conversation, we explore:

  1. Why the West outsourced PCB manufacturing to Asia in the 2000s and why bringing it back matters for American competitiveness

  2. What Shenzhen’s manufacturing culture does better than Silicon Valley (and what the U.S. can learn from it)

  3. How Diode’s models can one-shot much of schematic design and compress hardware timelines from months to weeks

  4. The three-week YC pivot that transformed Diode from a design validation tool into a full-stack manufacturer

  5. Why circuit boards are the “forgotten middle child” between silicon and software

  6. How Diode partners with Anthropic to make LLMs better electrical engineers

  7. What it takes to build a hardware company in 2025—and why the talent bar must stay incredibly high

  8. How Italian, American, and Chinese cultures shaped Davide’s approach to entrepreneurship and manufacturing

Thank you to the partners who make this possible

.tech domains: An identity for builders at their core.

Guru: The AI source of truth for work.

Brex: The intelligent finance platform.

Explore the episode

Timestamps

(00:00) Intro

(04:15) Why Davide calls himself a copper merchant

(05:53) Diode’s mission to rebuild PCB manufacturing in the U.S.

(07:58) What success looks like

(09:00) Growing up in northern Italy and spending a year in Minnesota

(13:14) Why Italy produces fewer venture-backed founders

(15:30) Why Hong Kong accelerated Davide’s learning

(19:09) Silicon Valley vs. Shenzhen

(22:05) What Davide learned in Apple’s Special Projects Team

(24:11) Why Davide left Apple after two years

(26:54) Meeting his co-founder, Lenny

(29:32) How Davide uncovered the need for better PCB design and manufacturing

(33:23) PCB manufacturing in Asia, and Diode’s approach

(41:29) The YC pivot that changed Diode’s business

(44:39) Inside Diode’s customer journey

(48:10) Where the value is in electronics manufacturing, and Davide’s AGI thesis

(51:30) What separates a working board from a great one

(55:32) Where Diode fits in the electronics stack

(59:55) Diode’s early near-death moment and long-term vision

(1:02:30) Diode’s exceptionally high bar for hiring

(1:04:48) Where Davide gets his best ideas

(1:07:00) Final meditations

Follow Davide Asnaghi

LinkedIn: https://www.linkedin.com/in/d-asnaghi

X: https://x.com/davideasnaghi

GitHub: https://hexdae.github.io

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Production and marketing by penname.co. For inquiries about sponsoring the podcast, email jordan@penname.co.

]]><![CDATA[Investing Like A Mystic: How Cyan Banister Finds Outliers (Co-Founder of Long Journey Ventures)]]>https://www.generalist.com/p/investing-like-a-mystic-cyan-banisterhttps://www.generalist.com/p/investing-like-a-mystic-cyan-banisterTue, 05 May 2026 12:03:43 GMT

“AI is going to be the age of the polymath. If you have the ability to think about different systems and how they might work together, you’re going to be able to come up with outcomes that were previously impossible because those disciplines didn’t work well together.” — Cyan Banister, Co-Founder of Long Journey Ventures

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Cyan Banister has built one of the most distinctive early-stage track records of the last fifteen years, with early bets on companies like Uber, SpaceX, DeepMind, Niantic, and Postmates. Today, she is co-founder and general partner at Long Journey Ventures, where she backs what she calls “magical weirdos.” Banister describes herself as a professional daydreamer, running constant thought experiments and paying close attention to signals others ignore. In this episode, she explains how that mindset translates into investing, and why many of her best opportunities have come from observation, curiosity, and a willingness to look in unlikely places.

In our conversation, we explore:

  • Cyan’s philosophy of treating life as a series of experiments

  • The strange, profound experiences that led her to question and ultimately move beyond her atheism

  • How scanning Wi-Fi networks in a Four Seasons café led her to Flock Safety, last valued at $8.4 billion

  • Long Journey Ventures’ “Biz, Tizz, and Rizz” framework for identifying exceptional founders and why the trifecta is rare

  • How AI will enable the age of the polymath

  • Why she believes brain-computer interfaces are closer than most people think

  • Why she says Pokémon Go was “the closest we ever came to world peace”

  • Why she lives part-time in a retirement community and her vision for a more connected future

Thank you to the partners who make this possible

.tech domains: An identity for builders at their core.

Brex: The intelligent finance platform.

Persona: Trusted identity verification for any use case.

Explore the episode

Timestamps

(00:00) Intro

(03:51) Never playing the game you appear to be playing

(07:18) Practicing childlike wonder as a daily discipline

(10:08) Questioning belief after her stroke

(13:30) Cyan’s metaphysical experiments

(23:24) Non-local consciousness and creativity

(27:22) Investing with extreme openness to signals

(29:05) The importance of timing in investing

(32:26) Meeting Travis Kalanick

(34:19) Finding Flock Safety through a chance encounter

(38:23) The summer of Pokémon Go (what worked and what didn’t)

(39:55) Human nature and what makes something “stick”

(42:15) Brain-computer interfaces and AI’s accelerating effect

(52:53) “Biz, Tiz, Riz:” her framework for evaluating founders

(59:20) Why Cyan lives in a retirement community part-time

(1:03:50) A unique way of finding books that speak to you

(1:08:44) Final meditations

Follow Cyan Banister:

LinkedIn: https://www.linkedin.com/in/cyanb

X: https://x.com/cyantist

Newsletter: https://uglyduckling.substack.com

Website: https://cyanbanister.com

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]]><![CDATA[The Writer-Researcher’s Guide to Claude Code]]>https://www.generalist.com/p/the-writer-researchers-guide-to-claudehttps://www.generalist.com/p/the-writer-researchers-guide-to-claudeThu, 23 Apr 2026 13:41:25 GMT

Friends,

Sometime, about three months ago, I had a realization. It may sound obvious to many of you, and absurd to others.

It is this: the Claude models released in December (and improved upon since) have driven the greatest personal capability leap in my lifetime.

The iPhone changed the way I accessed information and navigated the world. Social networks altered how I communicated and fraternized. Any number of apps and programs improved my workflows, smoothed opportunities, and accelerated my thinking. Doubtless, dozens of quieter improvements have changed the texture of my life without me realizing it — superior medicines and more efficient machines.

But as a single, discrete jump, nothing else comes close. Before Opus 4.5 — Claude’s winter upgrade — I had opened my computer’s terminal on a handful of occasions, mostly by accident. The last time I’d actually tried to use it had been a 2015 continuing education course at General Assembly that I’d bungled through with the grace of a macaque running a printing press.

Today, I spend more than 70% of my working hours in the terminal. I build software, construct systems, and conduct sprawling research assignments. Agents parse hundreds of articles, looking for tidbits aligned with my interests or writing goals, and prepare reports in my preferred style. Others streamline administrative duties, find bugs, polish edges, and ship the next feature I have in my mind.

The result is a system that increasingly feels not just like an extra employee, but closer to 20.

One of the challenges of running a business like The Generalist is that you cannot play just one role. I may get the greatest pleasure from sitting in front of an empty screen and beginning to write, but there are bills to be paid, scheduling to do, dashboards to survey, growth strategies to deploy, sponsors to consider, and emails to send. Every day is a battle between deep work and the realities of running a business. It is an unavoidable fact that every hour spent on these things is one I cannot spend on the parts I enjoy most and where I feel I have the greatest advantage.

Even outside these especially mundane tasks, there are thousands of little irritations and distractions. Try to conduct detailed research on a person or topic, and count the moments of friction. How many paywalls will you hit for publications you already purchase? How many pop-up ads or cookie advisories? What purposefully distracting news carousel will spin across the bottom of your article? What video will stuff itself in from the side? If you want to study someone’s background on LinkedIn or Twitter, how many red notifications will raise themselves like welts? How many DMs call for your attention? What new product announcement screams for you from the feed? How many greedy fingers try to grab your focus in the simple act of trying to travel from A to B, from intent to information?

The modern internet has become an attention casino, and we have grown accustomed to working in the middle of it, averting our eyes from clanking slots and spinning wheels. But what if your workspace could feel more like a quiet desk in a well-lit library? What if you could dispatch 20 virtual employees to wade through the morass of the modern web for you and deliver the results? What if you could cut back on the logistics and wrangling and give yourself the time to focus on the one thing you do not want AI to do for you? What if you could double the time you spent in deep work?

Over the past few months, I have been using Claude Code to explore these questions for myself. The result is a full-stack knowledge management system spanning multiple software products, internal systems, and a local model. (I have built several other things, but this is the crispest distillation.) It is not perfect, nor will it be for everyone. Showing these systems to friends and family over the past months, I’ve found that it often helps people better understand what is now possible with these models, and how you might use them to your benefit.

When I’ve caught up with these people days or weeks later, I’ve often found they’ve undergone a similar transformation, from dabbler to devotee. While there’s undoubtedly a risk in outsourcing too much, including the act of thinking itself, so far I haven’t found this to be true for myself or others. Rather, people seem to be having an extremely fun time, building systems that take away drudgery and interfaces explicitly shaped for how their minds work. It’s incredible how much easier and more pleasant it is to navigate an app that intuitively works the way your brain does, rather than one constructed for the general population.

If you’re curious about these tools, I would recommend setting yourself the goal of building the smallest possible thing you can think of. You’ll quickly find your ambitions grow as your comfort sitting in front of a terminal does.

With that, here’s a look at my system and how it’s changed my workflow.

The Library

There is a particular type of cognitive annoyance which one might call “thing-finding.” You have undoubtedly experienced it. It is the moment when you’re forced to stop in the middle of something and ask, “Agh, what was that thing again? That thing that person said? That thing I read? That thing I wrote? Did I save it somewhere? Is it in Google Drive? Will Finder locate it? (Nope!)”

An occupational hazard of running a media company is that there are a lot of things. There are research things and podcast things and article things and note things and PDF things and email things and interview things. And they are invariably quite hard to find. Historically, they have been spread out across my desktop, Google, Obsidian, Ulysses, The Generalist’s website, and Dropbox. In the grand scheme of the travails of the world, the callous mysteries of the universe, dark matter, and the infinite unknown, I recognize this is not a real problem. But when you are in the middle of a piece, just beginning to find that fragile rhythm that even an ambling ice cream truck can disrupt, this is among the most annoying interruptions possible.

Delphi, the all-seeing oracle, is the solution I’ve built to address this issue.

To start, I compiled every piece The Generalist has ever written, the transcripts of every podcast I have recorded, my full compendium of Obsidian notes, my Readwise highlights, a good chunk of Google Drive, and assorted files from my desktop. In total, there are more than 45,000 searchable “chunks,” with more added by the day.

The search pipeline relies on three layers, fused together: vector search via Voyage-3 embeddings, keyword search via SQLite FTS5, and a locally trained cross-encoder reranker, distilled from Cohere. I created the reranker by starting with a compact, open-source model pre-trained on Microsoft’s MS MARCO search dataset and fine-tuning it on nearly 40K query-passage pairs from our data. That taught the model what “relevant” looks like from our initial Cohere setup, and allowed Delphi to deliver high-quality results more quickly and cheaply.

If you don’t know what this means, don’t worry. It’s absolutely not necessary to get this deep into the weeds. I certainly didn’t expect to fine-tune even a tiny model of my own, but bit by bit, you start to become interested in what else you can do. The system above is, I’m sure, far from perfect, but it’s working well for me at the moment.

Now, using Delphi, I can type in a half-formed query like “remind me what philosopher Karol talked about on the podcast,” and it will recall that Karol Hausman, CEO of Physical Intelligence, shared his interest in Spinoza.

If I want to ask something that requires referencing multiple sources—for example, what CEOs have said about their hiring practices—it can handle that too, pulling in references from across my corpus.

I could have answered these questions in the past. The first one would have taken me a few minutes of pecking and tabbing; the second perhaps several hours. In all likelihood, I simply wouldn’t have bothered.

As a destination, I don’t use Delphi that often, and I still think it can be greatly improved. The UX isn’t quite as polished as I would like, and search is good, but could still be faster and smarter. But it mostly does what I want it to, and it’s reassuring to know that it’s there when I need it. As you’ll see, its power is leveraged in our other tools.

The Researchers

The most useful thing I have built is not visible, nor easily explicable. It is a concatenation of skills, tools, techniques, and preferences that allow me to gather information widely and thoroughly, without having to do the hunting and pecking myself.

Fundamentally, my research system operates through a collection of agents dedicated to snuffling out relevant information from particular mediums. One scans my internal corpus for existing work. Another reads relevant articles. A third seeks out podcast appearances.

Like Liam Neeson’s protagonist from Taken, each of these has been equipped with a “particular set of skills” that aid them in their quest. Jina and Firecrawl turn webpages into clean, readable text. An open-source tool searches YouTube and pulls crisp transcripts of interviews. Various scripts search for a subject’s published writing (a blog or personal webpage) or secondary media appearances. A headless browser allows agents to access articles on paywalled sites for which I have subscriptions. Instead of having to check The Financial Times, The Economist, and The New Yorker one by one, the agent can do it for you, as long as you’re logged in.

Crucially, it can do all of this in the background while you work on something else. Not only have you avoided the attention pitfalls and switching costs imposed by the modern web, but you’ve effectively hired a capable research assistant. I had always hoped The Generalist would grow large enough for it to make sense for me to hire such a person; now, I have been granted a dozen, each with a very strong grasp on what I am likely to find most relevant.

To ensure the research is conducted at a high standard, I’ve created a set of skill files for these agents to reference. These include dictums such as:

Read more

]]><![CDATA[The Future Of Drug Discovery Is 4 Billion Years Old (Viswa Colluru, Founder & CEO at Enveda)]]>https://www.generalist.com/p/the-future-of-drug-discoveryhttps://www.generalist.com/p/the-future-of-drug-discoveryTue, 21 Apr 2026 12:04:20 GMT

"Most often, we tend to conflate innovation and novelty. But if I look around, most things that have changed the fabric of our lived experience were actually not new when they did." — Viswa Colluru

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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

Thank you to the partners who make this possible

Brex: The intelligent finance platform.

Ahrefs Brand Radar: Find your brand in AI results.

Persona: Trusted identity verification for any use case.

Explore the episode

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

Follow Viswa Colluru

LinkedIn: https://www.linkedin.com/in/viswacolluru

X: https://x.com/viswacolluru

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Production and marketing by penname.co. For inquiries about sponsoring the podcast, email jordan@penname.co.

]]><![CDATA[How a 20-Person Startup Won Gold at the Math Olympiad—Tying With OpenAI & DeepMind (Tudor Achim, CEO of Harmonic)]]>https://www.generalist.com/p/how-a-20-person-startup-won-goldhttps://www.generalist.com/p/how-a-20-person-startup-won-goldTue, 14 Apr 2026 12:03:42 GMT

"Within 2 or 3 years, AI mathematicians will surpass human mathematicians for any specific mathematical task. I don't think it'll be a decade, like some people say." — Tudor Achim, CEO of Harmonic

Listen or watch now on
YouTube, Spotify, or Apple Podcasts

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

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.

Explore the episode

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

Follow Tudor Achim

LinkedIn: https://www.linkedin.com/in/tudorachim

X: https://x.com/tachim/with_replies

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Production and marketing by penname.co. For inquiries about sponsoring the podcast, email jordan@penname.co.

]]><![CDATA[The Secret Art of Elicitation]]>https://www.generalist.com/p/confidentialhttps://www.generalist.com/p/confidentialFri, 10 Apr 2026 13:27:57 GMT

Friends,

You didn’t mind speaking with Hanns Scharff. No, it was better than that — you liked it. He spoke English well, for one thing, the product of years spent in South Africa and a wife from London. But that didn’t fully explain it either. He spoke softly (unlike the others), was good for a joke or a story, and when he directed his dark, thoughtful eyes in your direction, you didn’t feel fearful, but at ease.

You could almost forget that he was the Luftwaffe’s most effective interrogator. And you, his prisoner.

While the Nazi Party’s other torturers and wheedlers relied on threats and violence, Scharff found success with a more genteel approach, taking downed pilots on long walks in the Taunus hills northwest of Frankfurt, during which he seemed to avoid discussing military matters. Only later, in some cases much later, would prisoners realize what had happened.

One American pilot recalled such a stroll. Only after he and Scharff had wandered and chatted for a while did the German mention, almost in passing, that a chemical shortage seemed to have impacted American munitions: their tracer bullets now trailed white smoke rather than their usual red.

No, no, the American told him. That wasn’t caused by a chemical shortage; it was a matter of design. American tracer bullets shifted from red to white when a pilot was running low on ammunition. It was a kind of warning system.

There it was. By purposefully saying the wrong thing, Scharff prompted the pilot to correct him, all without asking a question. A pocket had been picked without the wallet’s owner even registering a rustle. The pilot would not realize what had happened until it was much too late.

This technique, and others like it, are the topic of Confidential by John Nolan, a 1999 book that is as fascinating as it is difficult to obtain. Though prized by intelligence officials and professional “elicitors,” Confidential is no longer in print and only available to buy second-hand. To grab my copy, I spent a few hundred dollars on eBay and waited weeks for it to arrive. All of which only adds to its strange allure, as if someone decided Nolan’s work was too useful to simply leave lying around.

Confidential is a wildly entertaining and impressively insightful book. In studying it closely these last few months, I’ve also come to believe it’s an important one. Though Nolan is ostensibly writing for the professional intelligence gatherer, his conversational techniques are useful to anyone, in any context. They are liable to make you more engaging and persuasive, as well as a better conversationalist.

It is also worth knowing when someone else is using them. Why did that salesperson seem to purposefully misspeak? Was I imagining it, or did that headhunter seem to disbelieve everything I said? What is it about this person that makes me want to open up so much? For founders working in sectors of national interest, Confidential will help you protect what you know. If you are building almost anything of note, there is a good chance that someone out there — whether in a bland concrete building, a glassy office tower, or a grassy tech campus — would love to understand it better than you’d like them to.

This is the second piece in an occasional series about books that change how you see everyday interactions. The first, on Keith Johnstone’s Impro, explored the invisible power dynamics in every conversation. Confidential picks up the other side of that coin: how information actually moves between people, and what a former spy figured out about making it move faster.

If you’d like to read more work like this — pieces that dig into overlooked ideas and the people behind them — a premium membership is $22/month or $220/year. Subscribe now.

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Lessons from Confidential

Like a character in Atlas Shrugged, the natural question when beginning Confidential is, “Who is John Nolan?”

The first thing to say is that this is the name you would want for a spy — a vaguely heroic sound being stirred into a bowl of porridge.

There are, fittingly, few details online, so we must rely on Nolan’s own telling. For twenty-two years, he worked as a spy. From the sparse available details, it seems Nolan spent time in some of the intelligence community’s more controversial programs, a background that lends Confidential both its authority and occasional chill.

After his time working for the government, Nolan founded a corporate espionage consultancy that advised business clients and gathered intel on their behalf. (One of the only articles I can find that mentions Nolan outside of Confidential covers an espionage campaign conducted by P&G against Unilever in 2000 to obtain the “secrets of shampoo.” Nolan’s firm was ostensibly the orchestrator.)

As part of his work, Nolan’s team relied on the psychological tools he outlines to extract sensitive information — all while being perfectly explicit about who they were. Beyond his team, Nolan also trained executives to use his techniques and protect themselves against him.

Nolan’s own “call to adventure” is a memorable starting point for the book. In 1960s New Jersey, Nolan started work as a typewriter salesman for a small local outfit, competing with a rep from IBM. Despite offering a superior product, Nolan struggled to shift them. While he flailed around trying to convince companies to have him in for an appointment, his rival was having companies call him - everyone knew that if you needed a typewriter, you went to “Big Blue.”

Sitting in a coffee shop one day, Nolan watched as the IBM salesman loaded typewriters into his station wagon. “In a brief moment of clarity,” Nolan came to a realization. Why bother to hunt and scrabble for customers when he could simply follow his rival and figure out who was in the market for typewriters?

For the rest of the day, he tailed the station wagon, watching it go from office to office. The next morning, he set out and visited every company, one after another, showcasing what his product could do. That week, he sold twelve typewriters.

Over the following months, he repeated the trick, shadowing the IBM rep a few days each week. He grew cocky enough to wait outside an office building and follow him an hour later. Without meaning to, Nolan stumbled into the world of intelligence gathering and had seen what it could yield.

This is the first of Nolan’s rollicking stories, but it would be wrong to classify this as a collection of yarns. Across Confidential’s 350 pages, Nolan outlines techniques of striking psychological acuity, interleaved with lessons from the history of espionage, and detailed examples. On a given page, you’re just as likely to learn about the subterfuges Johnson & Johnson deployed to defend the Tylenol market as to analyze the brilliant sinuousness of Sherlock Holmes’s questioning style.

For this piece, we’ll focus mainly on Part I: “Eliciting the Information You Want and Need.” Though the latter two parts offer interesting details, they primarily address how organizations can collect intelligence more effectively or protect against spies.

As the title of Part I suggests, it covers the art of “elicitation.”

Even if you are familiar with this word, its place in the Nolan lexicon is particular and benefits from definition. When the author writes about elicitation techniques, he explicitly means the following:

Elicitation…is defined as that process which avoids direct questions and employs a conversational style to help reduce concerns and suspicions—both during the contact and in the days and weeks to follow—in the interest of maximizing the flow of information.

As Nolan explains, elicitation is expressly distinct from interrogation and interviewing. “Interrogation [is] obtaining what you want from someone who possibly has it, who has not admitted to having it, and who knows who you are and why you want it,” he writes. Meanwhile, “interviewing is the process of obtaining information from someone who probably has it, who has more or less admitted to having it, and who knows who you are and why you want it.” Interrogation is, by definition, adversarial, while interviewing tends not to be.

As you’ll see, elicitation is a subtler dance.

The worst way to get an answer might be a question

Read more

]]><![CDATA[30% Of Network Engineers Are Retiring. What Happens Next? (Anil Varanasi, Co-Founder & CEO of Meter)]]>https://www.generalist.com/p/the-case-for-autonomous-networkshttps://www.generalist.com/p/the-case-for-autonomous-networksTue, 07 Apr 2026 12:04:26 GMT

“I don't think Silicon Valley knows anything about networking anymore.” — Anil Varanasi

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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

Thank you to the partners who make this possible

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Explore the episode

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

Follow Anil Varanasi

LinkedIn: https://www.linkedin.com/in/anilcv

X: https://x.com/acv

Website: https://anilv.com

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]]><![CDATA[The Satya Nadella Playbook]]>https://www.generalist.com/p/satya-nadellahttps://www.generalist.com/p/satya-nadellaThu, 26 Mar 2026 14:47:36 GMT
Illustration by Tyler Comrie

Friends,

If Silicon Valley has any religion, it is that of the founder. Nowhere else puts as much faith in, nor grants as much latitude to, sovereign individuals attempting to build something from scratch. Only within this strip of approximately 35 miles might a broke 20-year-old in pajama pants and Adidas sliders command greater reverence than a celebrated researcher, diligent doctor, or decent executive. This is the strangeness of Silicon Valley and its genius.

The cult of the founder has enjoyed a fresh, febrile burst. Now, more than anytime in the last decade, operating in “founder mode” — the term popularized by Paul Graham’s post — is seen as synonymous with efficacy. Managers (that wretched, blighted species) are viewed not only as less productive but less legitimate, usurpers and meddlers that merely disrupt the glowing chi that stems from the central chakra of those who build.

Look across the tech landscape, however, and there is one manager that bears closer inspection: Satya Nadella. Since his appointment as Microsoft CEO in 2014, few executives boast a more impressive record. Given Microsoft’s current strengths, it is easy to forget the company Nadella inherited. Unlike Tim Cook, who stepped into an innovative organization still in the early innings of capitalizing on a new product category, Nadella stepped into a company that was culturally rotten, creatively blocked, and stuck with a sideways stock price. It is true that Ballmer had sown the seeds for a cloud computing renaissance, as we’ll discuss, but this was far from the finished article.

In the intervening 12 years, Nadella not only drove the company to a $3 trillion market cap but also oversaw an authentic internal revolution, expanded its product suite, and positioned Microsoft to keep pace in the AI era. He has done so while portraying himself as the consummate modern manager, fond of borrowing from the Buddha, and peddling the MBA-circuit bon mots of empathetic leadership and a “growth mindset.” Nadella’s own chronicle of his turnaround, Hit Refresh, is stuffed with such cheery banalities. While the great CEOs of the past and current generation are prone to fits of rage, savage dressing-downs, and impossible expectations, Nadella appears genuinely reasonable, a happy guru who would like you to work hard, sure, but don’t forget to take time for your family and maybe a restorative hobby.

How has he done this? How does a peacetime CEO win in a war zone? Can one really win at this scale without the animal intensity of Musk or Huang? Is the balmy public presentation the whole story?

To answer these questions, I have spent the past three months studying Nadella’s leadership from as many angles as possible. That includes Hit Refresh, Acquired’s two-part series on Microsoft before Nadella, a slew of podcasts and long-form articles, internal emails released in court filings, annual shareholder letters, and confidential expert interviews with former Microsoft executives.

What emerged is a nuanced portrait of how a manager built fresh power structures beneath him, constructed new mythologies, reset cultural norms, and developed founder-like authority.

This piece is part of The Generalist’s ongoing series of managerial “playbooks,” exclusively available to premium subscribers. You can find our previous editions on Elon Musk, Jeff Bezos, and Jensen Huang here.

Our mission, across all of these playbooks, is to reveal the real strategies legendary entrepreneurs use to build their businesses. These are often uncomfortable and in direct conflict with traditional managerial advice. However, if you believe progress depends on innovation, as we do, then understanding these principles, foibles included, is not only interesting but essential.

To unlock all four playbooks and everything else The Generalist has to offer, join us now for $22/month. You’ll get immediate access to our best long-form writing, company case studies, exclusive interviews, and private databases.

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Here is the Satya Nadella Playbook:

  1. Manifest authority through mythology.

  2. Borrow power from the old regime (even as you counterposition against it).

  3. Remake the aristocracy beneath you.

  4. Make it safe to fail.

  5. Once the narrative is set, use it as cover.

  6. Hone your sharpest knife.

  7. If you can’t win the future, at least don’t lose it.

In each section, we’ll unpack the strategies behind these principles and outline their benefits and tradeoffs.

What to expect

  • A 10,000+ word playbook of tech’s most effective non-founding CEO

  • How Nadella earned founder-like authority without founding anything

  • How Nadella dismantled Microsoft’s infamous stack ranking culture

  • The bathroom-break decision that opened Azure to Linux

  • The $2.5 billion acquisition that had nothing to do with productivity (and everything to do with distribution)

  • The licensing maneuver that imposed a 400% tax on competitors’ cloud customers

  • How a panicked 2019 email led to the $13 billion OpenAI bet

  • Over 100 hours of research, confidential executive interviews, and court filings distilled

…and much more. To unlock the full playbook and learn how a “safe pick” turned a stagnant giant into a $3 trillion force, join our premium newsletter today.

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Part I: The Inheritance

By definition, a non-founding CEO does not start from scratch. They enter an environment of someone else’s making and must transform it into something of their own. To understand how Satya Nadella changed Microsoft then, we must first grasp the state of the company he inherited. It was one just emerging from what became known as its “lost decade.”

A stalled stock

When Steve Ballmer stepped into the CEO role in January 2000, he was taking the reins of the most valuable company on the planet. Less than three weeks earlier, Microsoft had hit a peak valuation of $615 billion, with a stock price approaching $60.

When the crash came, Microsoft cratered, dropping below $250 billion. It was not the fall that was remarkable, but what happened after. Or rather, what didn’t happen after. In the years that followed, as other wounded tech players stabilized and then climbed, Microsoft stayed stuck, even as its underlying performance improved. During Ballmer’s reign, revenue compounded from $23 billion to $86 billion while operating income improved from $11 billion to $28 billion. And yet, the stock barely moved, flatlining at about $30 a share. Over a similar timeframe — between late 2000 and mid-2012, Apple snowballed from a $4.8 billion pipsqueak into a $541 billion behemoth. By the time Nadella’s reign began, Microsoft was firmly in its shadow.

Read more

]]><![CDATA[Why One Superintelligence Is More Dangerous Than a Thousand (Vincent Weisser, CEO & Co-Founder of Prime Intellect)]]>https://www.generalist.com/p/why-one-superintelligence-is-morehttps://www.generalist.com/p/why-one-superintelligence-is-moreTue, 24 Mar 2026 12:05:42 GMT

“I would argue the biggest risk is actually locking in a very narrow monoculture for superintelligence. One superintelligence is much less safe than infinite superintelligence.” — Vincent Weisser

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YouTube, Spotify, or Apple Podcasts

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.

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

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Explore the episode

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

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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Production and marketing by penname.co. For inquiries about sponsoring the podcast, email jordan@penname.co.

]]><![CDATA[Why Robots Still Struggle With Simple Tasks (And What Might Finally Change That) | Karol Hausman, Co-Founder & CEO of Physical Intelligence]]>https://www.generalist.com/p/karol-hausman-physical-intelligencehttps://www.generalist.com/p/karol-hausman-physical-intelligenceTue, 17 Mar 2026 12:03:46 GMT

Listen or watch now on
YouTube, Spotify, or Apple Podcasts

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

Thank you to the partners who make this possible

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Granola: The app that might actually make you love meetings.

Explore the episode

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

Follow Karol Hausman

LinkedIn: https://www.linkedin.com/in/karolhausman

X: https://x.com/hausman_k

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Production and marketing by penname.co. For inquiries about sponsoring the podcast, email jordan@penname.co.

]]><![CDATA[America’s Electric Power Grid Is Broken. This Startup Is Trying to Fix It. (Zach Dell, co-founder & CEO of Base)]]>https://www.generalist.com/p/americas-electric-power-grid-is-brokenhttps://www.generalist.com/p/americas-electric-power-grid-is-brokenTue, 10 Mar 2026 12:03:39 GMT

“You must get comfortable with the notion that a lot of really smart people you respect are going to explain to you in excruciating detail why this is not going to work...You have to be able to see through that and have a clear vision in your mind for why it is going to work.” — Zach Dell, co-founder and CEO of Base

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For decades, America’s electrical system has rewarded utilities for building more infrastructure, not for lowering costs. The result is a grid that expanded but rarely improved. Zach Dell, co-founder and CEO of Base, is building a different kind of power company. In under three years, Base has grown into a vertically integrated business valued in the billions. It combines home batteries and software to store electricity when it is cheap and deliver it when demand spikes. Dell’s interest in energy began long before Base. In college, he tried to lease a Hawaiian lava field for a solar project. He also experimented with anaerobic digestion systems in India and worked at Blackstone and Thrive Capital, where he met his co-founder. His bet is simple but ambitious: the next phase of the grid will come from increasing utilization rather than constantly building new infrastructure.

In our conversation, we explore:

  • How a failed college solar project and early energy experiments in India pulled Zach into the power industry

  • The lessons he absorbed from his parents, including truth-seeking, reinvention, and competitive endurance

  • How the U.S. grid’s regulatory structure discourages innovation and why Texas’s deregulated market creates space for new power companies

  • Why batteries are best understood as a time-shifting technology that increases grid utilization and reduces total system costs, not simply as energy generators

  • Base’s “make, move, store, sell” framework for thinking about the full power stack

  • How Base aims to become the first beloved energy company

  • How Zach identified Justin as a world-class operator and built the trust needed to go all-in together on a non-obvious idea

  • How aggressive AI adoption is compressing cycle times and why slow adopters risk falling behind

Thank you to the partners who make this possible

Granola: The app that might actually make you love meetings

Brex: The intelligent finance platform.

Explore the episode

Timestamps

(00:00) Introduction to Zach Dell and Base

(03:06) The Hawaiian lava field solar project and early energy curiosity

(07:58) Investing vs. operating

(09:31) Lessons from Phil Jackson on aligning talented teams

(15:24) Lessons from his parents

(19:19) The loneliness of solo founding and the value of co-founders

(21:49) Justin’s strengths as a co-founder and how their partnership formed

(30:55) Why Base became the obvious focus

(32:21) The original vision and the three reversals

(35:49) The American power grid and what makes Texas different

(40:39) Why batteries matter and what Base is building

(41:44) How Base works in two market types

(45:59) Base’s core product

(47:43) The software behind Base’s battery network

(49:14) Base’s partnerships with battery cell makers

(50:43) The Gen 2 hardware mistake and the lesson in risk management

(52:00) Dino’s strengths as Head of Hardware

(53:20) Base’s positioning as grid infrastructure

(55:39) Building a beloved energy brand

(58:45) How hiring at Base has evolved

(1:01:54) AI workflows at Base

(1:03:44) Zach’s dedicated deep work time

(1:06:29) Final meditations

Follow Zach Dell

LinkedIn: https://www.linkedin.com/in/zach-dell-a631a554

X: https://x.com/ZachBDell

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Production and marketing by penname.co. For inquiries about sponsoring the podcast, email jordan@penname.co.

]]><![CDATA[Infinite Games]]>https://www.generalist.com/p/infinite-gameshttps://www.generalist.com/p/infinite-gamesThu, 26 Feb 2026 14:58:49 GMT

“To be prepared against surprise is to be trained. To be prepared for surprise is to be educated.”

- James Carse, Finite and Infinite Games

Friends,

Evolutionary biology has the concept of “allopatric speciation,” the process that occurs when a species develops separately from those on the mainland. Saved from some evolutionary pressures and exposed to oth…

Read more

]]><![CDATA[Everyone Is Betting on Bigger LLMs. She's Betting They're Fundamentally Wrong. (Eve Bodnia, Founder & CEO of Logical Intelligence)]]>https://www.generalist.com/p/everyone-is-betting-on-bigger-llmshttps://www.generalist.com/p/everyone-is-betting-on-bigger-llmsTue, 24 Feb 2026 13:03:32 GMT

“AGI should be just like natural intelligence. Something which plans, something which is able to predict, produce new knowledge, be cheap and efficient, and be adaptive to the environment. It should reason, it should not mimic.” — Eve Bodnia, Founder & CEO, Logical Intelligence

Listen or watch now on
YouTube, Spotify, or Apple Podcasts

Eve Bodnia is the co-founder and CEO of Logical Intelligence, which is developing energy-based reasoning models (EBMs) as an alternative to large language models. She argues that LLMs, which operate by recognizing and recombining patterns within language space, are structurally incapable of genuine reasoning. Eve's alternative: Kona—an EBM that reasons in abstract latent space, learns rules about the world rather than surface patterns, and can interface with language models as one output channel among many. Eve traces the core ideas behind her architecture to decades of work in symmetry groups, condensed matter physics, and brain science—fields that share, as she explains, the same underlying mathematics. In a public demo, Kona solved a complex reasoning task for roughly $4 in compute, compared to an estimated $15,000 using frontier LLMs. With Yann LeCun serving as founding chair of its technical board, Logical Intelligence sits at the center of a small but growing effort to rethink AI beyond language-based models.

In our conversation, we explore:

  • Why Eve believes LLMs can’t truly extrapolate knowledge, even at larger scale

  • What energy-based reasoning models are—and where the “energy” concept comes from

  • The $4 vs. $15,000 benchmark, and what it tells us about the cost of guessing vs. knowing

  • How Logical Intelligence showed spontaneous knowledge transfer at just 16M parameters

  • Why systems like chip design, surgical robotics, and power grids need more than probabilistic AI

  • What formally verified code generation means for the future of programming

  • Why the math behind particle physics also explains how the brain filters signal from noise

  • How meeting Grigori Perelman as a teenager shaped Eve’s views on ego and ownership in science

  • Why Eve believes humans must remain the constraint-setters in advanced AI

  • How meditation, piano, and Eastern philosophy support her creative process

Thank you to the partners who make this possible

Granola: The app that might actually make you love meetings.

Persona: Trusted identity verification for any use case.

Explore the episode

Timestamps

(00:00) Introduction

(03:03) Eve’s encounter with Grigori Perelman

(05:38) Why bizarre people are Eve’s favorite people

(06:56) Her early obsession with math and physics

(09:02) The manifold hypothesis and language

(11:54) The Kekulé Problem

(14:05) Eve’s upbringing and her CERN research in high school

(17:40) Eve’s academic path

(20:36) Symmetry in nature

(22:58) Spirituality and creativity

(27:00) Theory vs. experiment

(29:03) Uncovering a critical gap in AI models

(33:45) What Logical Intelligence is building

(35:50) Logical Intelligence’s use cases

(42:08) Energy-based models explained

(45:06) LLMs vs. EBMs

(48:01) AGI defined

(51:22) Kona’s knowledge extrapolation

(53:20) The team behind Logical Intelligence

(58:09) Early investors in Logical Intelligence

(58:50) Feynman’s influence on Eve’s work

(1:01:15) How Eve sustains her creativity

(1:03:42) Final meditations

Follow Eve Bodnia

LinkedIn: https://www.linkedin.com/in/eve-bodnia-351b41355

X: https://x.com/evelovesolive

Website: https://logicalintelligence.com

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]]><![CDATA[How Bolt Survived An 85% Revenue Crash And Became Europe's Ride-Hailing Champion (Markus Villig, Founder & CEO) ]]>https://www.generalist.com/p/how-bolt-survived-an-85-revenue-crashhttps://www.generalist.com/p/how-bolt-survived-an-85-revenue-crashThu, 19 Feb 2026 13:03:12 GMT

“We don’t care that we’re from a small country. We’ve always had the view that if we work harder and [make] smarter decisions, we can beat [any] company in the world, regardless of how big they are.”

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In 2013, on an Estonian island of just 10,000 residents, a teenager borrowed €5,000 from his parents and decided to take on Uber. Twelve years later, Markus Villig leads Bolt, a company operating in 50+ countries, generating nearly €3 billion in revenue, and standing as one of the only European tech companies competing at true global scale. Rather than going head-to-head with incumbents in their strongest markets, Bolt expanded through underserved cities, emerging economies, and overlooked segments of urban transport. When COVID erased 85% of its revenue in weeks, the company didn’t retreat; it staged a kind of corporate “eucatastrophe,” pivoting into food delivery across nearly 20 countries in what became a company-wide sprint. That same bias toward action now shapes Markus’s broader agenda: investing in defense tech for Estonia and Ukraine, pushing for capital markets reform, and advancing a contrarian thesis on autonomous vehicles.

In this conversation, we discuss:

  • How growing up in Soviet-occupied Estonia shaped Markus’s ambition and moral clarity

  • How Bolt’s European ethos and long-term focus on driver retention became a structural advantage

  • The marketplace models and capital discipline that allowed Bolt to outmaneuver better-funded rivals

  • Why Bolt found breakout success in African markets after failing in 12 Western countries

  • The 85% revenue collapse during COVID and the rapid food delivery pivot that reshaped the company

  • Bolt’s partnerships with Stellantis and Pony.ai and its long-term bet on autonomous vehicles

  • Why Ukrainian and Eastern European startups are often outperforming their Western peers

  • Markus’s blueprint for closing Europe’s tech deficit and building globally competitive companies

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Explore the episode

Timestamps

(00:00) Intro

(03:32) How The Lord of the Rings shaped Markus’s worldview

(05:52) Bolt’s underdog story and its existential turning point

(10:22) Estonia’s startup DNA and its imprint on Bolt

(13:38) Europe’s ambition problem

(17:23) Europe’s defense tech gap

(23:09) The need for capital market reform in Europe

(25:13) Bolt’s origin story

(36:35) Frugality as strategy

(38:24) What running Bolt actually demands

(41:27) The hidden costs of being too lean

(42:50) Bolt’s shift to experimentation

(44:10) Bolt’s micromobility strategy

(45:50) How Bolt found the right markets

(50:44) The Serbian mob story

(54:00) Markus on venture capital and lessons from Klarna’s board

(55:40) Why Bolt never sold

(57:08) Bolt’s autonomous vehicle (AV) strategy and key partnerships

(1:05:50) The concept of culture-market fit

(1:07:48) How Bolt operates: writing, hiring, reading, and more

(1:13:15) Markus’s personal strengths

(1:14:15) What people get wrong about business

(1:16:27) Final meditations

Follow Markus Villig

X: https://x.com/villigm

LinkedIn: https://www.linkedin.com/in/markusvillig

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]]><![CDATA[Lessons of a First-Time Fund Manager]]>https://www.generalist.com/p/lessons-of-a-first-time-fund-managerhttps://www.generalist.com/p/lessons-of-a-first-time-fund-managerThu, 05 Feb 2026 15:47:00 GMT

Friends,

Four years ago, I set out to raise my first venture fund. The result was Generalist Capital, a $15 million vehicle to invest in a small collection of (hopefully) legendary companies.

The beginning of this year marks something of a turning point. After closing on a few investments, Generalist Capital officially moved from the deployment phase to management and harvesting. That makes it a fitting moment to share the lessons learned along the way. Though venture capital is awash in content, in my view, there’s too little introspective writing on the mental game of fund management. Below, you’ll find my attempt to distill the most important lessons I’ve learned so far. To that end, these are best seen as the lessons of a first-time fund manager, relating primarily to the challenges of starting a fund and establishing an initial viable strategy.

Naturally, the gravity of these lessons will depend on information I cannot give you: my long-term performance as an investor. It will likely take another six to ten years to discover how promising a portfolio I have built with Generalist Capital, and even longer to know definitively if I am any good. As of our last update to LPs, Generalist Capital was performing in the top 10-15% in its vintage, but the data suggests that it takes about six years for a fund to settle into its terminal quartile. There’s still a long way left to run and too much uncertainty to read much into this. I am afraid that this is the best I can offer you.

Let this serve as a reminder, then, if any were needed, that you should take my lessons with a grain of salt. I have achieved little, and every investor must learn to play their own game. Though I hope they spark new ideas for you, these are just the observations of one manager at the beginning of what I hope proves to be a long career.

  1. If this is your first time managing a fund, be extremely skeptical of your early investment urges. You’re probably overly keen to validate your existence to your LPs.

  2. The best companies can go under the radar for a long time. When other VCs asked which of my investments I was most bullish about, I told them freely. It still took several years for a Tier 1 firm to lead a round into the company.

  3. You do not have to wait for a round. If you have enough to offer a company, they’ll find a way to bring you aboard ahead of a formal raise. When you see a startup you love, try to make something happen.

  4. Don’t assume you know your level. New managers often think they can’t get access to rounds led by Tier 1 VCs. With the right pitch, you may be closer than you think.

  5. It’s very rare you see everything you want in a startup: founder, market, traction, and so on. When you do, write a much bigger check than you ordinarily would.

  6. The first investor has a unique relationship with a founder. You can build good relationships no matter when you invest, but the first check in earns a special level of trust.

  7. Contrary to what you might expect, the best companies are not easier to access as round sizes grow. Competition escalates as they succeed, often through late growth.

  8. The best investors often need little more than a sentence to explain why they’ve invested in a company. The longer and more convoluted your investment rationale is, the more skeptical you should be of it.

  9. Find your pace. A generation of managers has been conditioned to deploy vintages in 18-24 months. There is nothing wrong with taking twice as long, or more.

  10. Just because a deal is hot does not mean it is good. Don’t let other people’s urgency influence your desire.

  11. Remind yourself: the best venture firms in the world make a majority of bad investments. Don’t index on a buzzy name.

  12. Conversely, if some of the best investors in the world are backing a startup you don’t consider promising, look again, extremely closely. There’s a good chance you’re missing something important.

  13. Remember, the best investors in the world ≠ the best known investors. Calibrate accordingly.

  14. Don’t assume the best opportunities will land in your lap. Outbound sourcing is a necessity.

  15. Progress is never linear. A company that you had more or less written off can suddenly catch light, and one that was on a breakout trajectory can easily stall or stumble.

  16. Don’t push your anxiety onto your founders. It’s not your job to let them know of every new company that pops up in their space.

  17. If you’re asked for advice, give it honestly, but recognize that you have 0.01% of the context a founder has.

  18. Fight for every dollar. Getting an extra $10K into a breakout company can make a huge difference for a small fund.

  19. Founders respond to conviction. Earnestly explaining to an entrepreneur why you’re excited about their business and showing a desire to move quickly can overcome any number of natural disadvantages.

  20. Be skeptical of a pitch that looks too rational. Great companies often have something paradoxical about them. If something doesn’t quite make sense, that’s a good indication you should lean in, not out.

  21. Be careful you don’t get stuck correcting your last mistake. If you invested too little in a startup that has just started to break out, don’t triple the size of your next check to compensate. Consider each opportunity anew.

  22. If you’re not positioned to catch a wave, don’t chase it. You could burn an entire vintage on second-and third-rate AI companies.

Read more

]]><![CDATA[The Private Company Bringing Nuclear Enrichment Back to America (Scott Nolan, CEO of General Matter)]]>https://www.generalist.com/p/the-private-company-bringing-nuclearhttps://www.generalist.com/p/the-private-company-bringing-nuclearTue, 03 Feb 2026 13:03:34 GMT

“We [the U.S.] went from controlling something like 86% of global enrichment capacity to effectively last place.”

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Roughly 20% of the U.S. power grid runs on nuclear energy. A quarter of the fuel behind it is headed toward a hard stop. In this episode, I sit down with Scott Nolan, founder and CEO of General Matter, to unpack why uranium enrichment has quietly become one of the most consequential industrial bottlenecks of the 21st century.

While at Founders Fund, Scott spent over a year searching for an American enrichment company to back. When he couldn’t find one, he decided to build it himself. Less than a year after emerging from stealth, General Matter secured a historic enrichment site in Paducah, Kentucky, and was awarded a $900 million Department of Energy contract—marking one of the first serious efforts to rebuild domestic enrichment capacity ahead of the 2028 ban on Russian supply.

In this episode, we discuss:

  • Why enrichment is the missing link in America’s nuclear supply chain

  • How the U.S. went from controlling 86% of global enrichment capacity to effectively none at commercial scale

  • The science behind uranium enrichment and why it matters for next-generation reactors

  • Why Scott applied the SpaceX playbook to nuclear after more than a decade in venture capital

  • How General Matter is revitalizing the historic Paducah, Kentucky enrichment site

  • The significance of General Matter’s $900 million Department of Energy contract

  • The bipartisan political support for expanding nuclear energy

  • Why Scott believes nuclear energy could grow 3-4x by 2050

  • The parallels between America’s space and nuclear industries

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Explore the episode

Timestamps

(00:00) Introduction to Scott Nolan

(02:24) General Matter’s mission to rebuild U.S. enrichment

(05:08) How the U.S. lost its edge

(06:24) The nuclear fuel cycle explained—and where enrichment fits

(08:31) Scott’s background: From SpaceX and Founders Fund to General Matter

(13:56) Lessons from SpaceX

(17:50) How Scott’s focus evolved over 13 years at Founders Fund

(21:00) How Scott landed on nuclear enrichment

(25:58) Why nuclear energy was off the radar—until recently

(30:10) Finding the right partner: Scott and Lee’s collaboration

(32:04) What downblending means and why it matters

(33:30) How U.S. uranium enrichment quietly came to an end

(38:34) The Russian uranium ban and the 2028 supply cliff

(40:38) How General Matter plans to compete

(43:09) Building a world-class team

(46:03) The market for enriched uranium

(49:30) Future bottlenecks

(50:56) What the U.S. needs to actually scale nuclear energy

(51:32) Uranium supply constraints

(54:16) LEU vs. HALEU: the fuels powering old and new reactors

(57:49) Why 20% enrichment is a critical threshold

(59:30) Why General Matter chose Paducah, Kentucky

(01:04:35) Legislation and executive orders easing nuclear friction

(01:07:06) The $900 million Department of Energy award

(01:11:08) Why mission matters most

(01:14:12) Final meditations

Follow Scott Nolan

X: https://x.com/ScottNolan

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]]><![CDATA[The Generalist’s Productivity Stack (2026)]]>https://www.generalist.com/p/productivity-stack-2026https://www.generalist.com/p/productivity-stack-2026Thu, 22 Jan 2026 15:41:21 GMT

Friends,

Last January, on a whim, I shared a few of the productivity methods I’ve found effective in maximizing my output while building The Generalist. It was driven by a geekish obsession with tiny optimizations, a love for adopting niche tools, and a real belief that the environment you make for yourself impacts the work you are capable of.

To my surprise, it became one of The Generalist’s most popular pieces. With January in full swing and the year still stretching in front of us, I decided to sit down and outline the new tools and methods I’ve found effective over the past year. It turned into quite a collection. You’ll find 26 tools I recommend (both digital and analog) and 8 practices I’ve found invaluable.

I’ve avoided repeating last year’s recommendations (though I still use them), so if you’re as into these kinds of hacks and optimizations as I am, you may want to read that piece, too.

Tools

An exhaustive list of the programs, apps, extensions, and physical objects I recommend.

Read more

]]><![CDATA[Programming Sunlight: How Reflect Orbital Is Building Satellites to Redirect Light From Space (Ben Nowack, Founder & CEO)]]>https://www.generalist.com/p/programming-sunlight-ben-nowackhttps://www.generalist.com/p/programming-sunlight-ben-nowackTue, 20 Jan 2026 13:03:59 GMT

“It’s either sunny or it’s not sunny, and there’s not much you can do about that—but we’re trying to change that for the first time ever.” —Ben Nowack

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Most energy conversations start with scarcity. This one starts with abundance. Sunlight powers nearly everything on Earth, directly or indirectly. And yet we have almost no control over when or where we get it. Ben Nowack thinks that’s a solvable problem. Ben is the founder and CEO of Reflect Orbital, a company building satellites designed to redirect sunlight from space—not as a thought experiment, but as a product. The company nearly died before it worked. Eight months in, Ben had $300 left and was living in a garage. He made a deliberate decision to go $50,000 into credit card debt to finish critical tests. At one point, he was down to $21 of available credit. A month later, Reflect raised its first round. Today, the company is preparing to launch its first revenue-generating satellites. This is a conversation about building conviction, finding the real market, and what changes when a fundamental resource becomes programmable.

In our conversation, we explore:

  • How Reflect’s satellites work

  • The surprising pivot from energy to lighting applications that made the business immediately viable

  • Ben’s remarkable journey from building RC planes and X-ray machines in high school to founding Reflect

  • Why previous attempts at space mirrors failed and what’s changed to make this possible now

  • The near-death moment when Ben went $50,000 into credit card debt to keep his vision alive

  • How Reflect plans to scale from moonlight-level brightness to potentially powering solar farms

  • The company’s first satellite launches planned for this year, and their path to a full constellation

  • The wide range of applications, from emergency response to municipal lighting to agriculture

Explore the episode

Timestamps

(00:00) Introduction to Ben Nowack

(02:26) What Reflect Orbital is building

(05:07) How the satellite constellation works

(08:00) What Reflect is launching this year

(10:35) Finding early markets

(13:43) Ben’s childhood and early building experiences

(22:04) What Ben learned working for startups

(28:03) High school projects: X-ray machines, rocket engines, and fusion reactors

(33:14) The eureka moment that led to Reflect

(35:24) Early validation of the idea

(38:35) The Russian space mirror experiments of the 1990s and what’s changed

(42:31) Partnering with Tristan Similac as co-founder

(45:05) Baiju Bhatt’s involvement

(47:04) Why Reflect isn’t pivoting to space-based data centers

(50:54) Common misconceptions about Reflect’s technology

(55:11) Why programmable light is valuable

(1:01:28) Initial target markets

(1:03:42) The future markets for Reflect

(1:07:33) Reflect’s company culture and operational philosophy

(1:12:05) Surprises and struggles in building Reflect

(1:14:56) Putting the idea to the test

Follow Ben Nowack

LinkedIn: https://www.linkedin.com/in/ben-nowack

X: https://x.com/bennbuilds

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Production and marketing by penname.co. For inquiries about sponsoring the podcast, email jordan@penname.co.

]]><![CDATA[Nothing’s Carl Pei on Building a $1B Smartphone Company, Why He Left OnePlus After 10 Days of Retirement, and Why He Thinks About Death Every Week]]>https://www.generalist.com/p/nothings-carl-pei-on-building-a-1b-smartphone-companyhttps://www.generalist.com/p/nothings-carl-pei-on-building-a-1b-smartphone-companyTue, 13 Jan 2026 13:03:26 GMT

“As humans, one of our superpowers is creativity. Both on an individual level, but also on a species level. And by creativity, I mean the ability to solve problems.” —Carl Pei

Listen or watch now on
YouTube, Spotify, or Apple Podcasts

Carl Pei is the founder of Nothing, the consumer electronics company known for its distinctive transparent design language across smartphones and audio products. Before launching Nothing in 2020, Carl co-founded OnePlus, where he spent seven years helping build it into a major smartphone brand. But Carl’s instincts as a builder showed up much earlier. As a teenager, he taught himself to code by building Pokémon fan sites, all while moving between China, the U.S., and Sweden. That combination of early creation and constant change shaped a founder comfortable with uncertainty—and deeply motivated by questions bigger than products. Carl thinks often about time and mortality, is skeptical of early retirement, and believes creativity is humanity’s real advantage. In an industry obsessed with optimization, he’s focused on making technology feel meaningful again.

In our conversation, we explore:

  • The origins of Nothing’s transparent design language and how it helps differentiate the brand in a mature, competitive market

  • Carl’s childhood fascination with mortality and how it continues to drive his ambition today

  • Nothing’s multiple near-death experiences, from 80% defect rates on first products to fundraising struggles

  • Why India has become a crucial market for Nothing’s smartphone business

  • How Nothing approaches community involvement, including letting users invest alongside VCs

  • The company’s approach to integrating AI features without overhyping the technology

  • Carl’s admiration for Genghis Khan’s management style and talent acquisition approach

  • The future of consumer electronics beyond smartphones

Thank you to our sponsor, Guru — The AI source of truth for work

Explore the episode

Timestamps

(00:00) Introduction to Carl Pei and Nothing

(02:44) Nothing’s long-term vision

(06:33) How existential thinking shapes Carl’s motivation

(10:12) Why Carl’s planned sabbatical ended after ten days

(12:35) Carl’s international upbringing

(16:02) Entrepreneurial experiments in China

(19:10) Carl’s competitive nature and attitude toward school

(25:30) Lessons from seven years at OnePlus

(28:07) Taking a break at age 31

(30:50) Carl’s fundraising strategy

(33:26) Why Carl chose London for Nothing’s HQ

(35:12) Lessons from Genghis Khan

(38:38) Nothing’s first near-death moment

(42:56) Nothing’s product evolution and breakout hits

(45:24) Partnering with Teenage Engineering

(49:28) Design inspirations

(51:36) How Nothing recruits talent

(53:42) Nothing’s approach to marketing

(56:51) How India became a key market

(59:48) Why Nothing created CMF

(1:02:12) Why Carl is bullish on India

(1:03:32) How Carl thinks about AI

(1:07:05) Rethinking ads based on community feedback

(1:09:05) How Nothing leverages community

(1:11:23) Why AI hardware is struggling

(1:13:37) Carl’s thoughts on the future of consumer electronics

(1:15:10) Philosophies that shape Carl’s worldview

(1:16:45) Final meditations

Follow Carl Pei

LinkedIn: https://www.linkedin.com/in/getpeid

X: https://x.com/getpeid

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Production and marketing by penname.co. For inquiries about sponsoring the podcast, email jordan@penname.co.

]]>