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“Our Goal Is to Build an Electrical Engineer.” (Davide Asnaghi, Co-Founder & CEO of Diode)

2026-05-19 20:01:48

"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

Resources and episode mentions

Books

People

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Production and marketing by penname.co. For inquiries about sponsoring the podcast, email [email protected].

Investing Like A Mystic: How Cyan Banister Finds Outliers (Co-Founder of Long Journey Ventures)

2026-05-05 20:03:43

“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

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

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

Resources and episode mentions

Books

People

Other resources

Subscribe to the show

I’d love it if you’d subscribe and share the show. Your support makes all the difference as we try to bring more curious minds into the conversation.

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Production and marketing by penname.co. For inquiries about sponsoring the podcast, email [email protected].

The Writer-Researcher’s Guide to Claude Code

2026-04-23 21:41:25

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

The Future Of Drug Discovery Is 4 Billion Years Old (Viswa Colluru, Founder & CEO at Enveda)

2026-04-21 20:04:20

"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

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

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

Resources and episode mentions

Books

People

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Production and marketing by penname.co. For inquiries about sponsoring the podcast, email [email protected].

How a 20-Person Startup Won Gold at the Math Olympiad—Tying With OpenAI & DeepMind (Tudor Achim, CEO of Harmonic)

2026-04-14 20:03:42

"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

Resources and episode mentions

Books

People

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I’d love it if you’d subscribe and share the show. Your support makes all the difference as we try to bring more curious minds into the conversation.

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Production and marketing by penname.co. For inquiries about sponsoring the podcast, email [email protected].

The Secret Art of Elicitation

2026-04-10 21:27:57

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.

Subscribe now

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

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