Jack Chan

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Jack Chan

Jack Chan

@jackchan_x

I study how to make better decisions and pitches. Dad of two. Boston lover.

Boston, MA Katılım Şubat 2022
209 Takip Edilen164 Takipçiler
Jen Zhu
Jen Zhu@jenzhuscott·
My formula for happiness: a Yes, a No, a straight line, a goal. - Nietzsche
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Andrew Yeung
Andrew Yeung@andruyeung·
Who is the expert at designing things without it looking like slop? Websites, apps, graphics, etc.
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clem 🤗
clem 🤗@ClementDelangue·
"Open weights are inherently secure" @sriramk
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Jerry Liu
Jerry Liu@jerryjliu0·
It does feel like every frontier lab is optimizing for general intelligence. This leaves room for everyone else to optimize for task-specific intelligence. Every task requires a different point on the cost-capability frontier. Optimizing for each task involves tuning the right set of models and harness for that task to push performance and reduce cost. I think there's plenty of room for open-weight models, optimization infrastructure (e.g. RL), vertical-specific workflows, and domain-specific models (e.g. us for document parsing) to thrive in this exponentially growing AI economy
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Jack Chan
Jack Chan@jackchan_x·
“Fund scientists, not universities” sounds like an easy choice. Scientists do the work. Universities take a cut and slow things down. Add AI and the race with China, and the answer seems obvious. But it’s a false choice. Scientists still need labs, equipment, research teams, and students. Most of that comes from institutions. Even the White House report doesn’t really choose one over the other. It proposes a mix of individual grants, universities, special research teams, and AI infrastructure. The real question is simpler: What is the best way to fund each problem? If success depends on one person’s ideas, fund the person. If it needs expensive equipment and a large team, fund the institution. If AI can produce faster results that can still be checked, use AI. The headline makes universities sound unnecessary. The actual decision is about which setup produces the best science.
Andrew Curran@AndrewCurran_

In an new report expected to be released later today the Trump administration will argue that directing federal funding towards individual researchers, and research that uses AI, will accelerate scientific progress faster than funding collages and universites.

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Jim Cramer
Jim Cramer@jimcramer·
We must NOT let our companies use these Chinese models to save a few bucks. OpenAI and Anthropic are correct. This is vital national security. Please read Bing West's just released Cat 5. I respect the Chinese people greatly but these companies are run by the PLA for heaven's sakes.
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Jack Chan
Jack Chan@jackchan_x·
The best way to win the debate over Chinese AI models is to change the decision. Ask the ban side: Can the U.S. really stop open model files from being copied? Probably not. Ask the no-ban side: Would you let an untested model run inside the Pentagon or the power grid? Probably not. Now both sides have given up their extreme position. The real decision is no longer “ban or don’t ban.” It becomes: Where should these models be allowed, and what should they be allowed to access? Keep normal use open. Put strict checks around government, defense, and critical infrastructure. That’s the persuasion lesson: When a debate gets stuck between two bad choices, don’t argue harder. Change the question to one people can actually decide.
Jim Cramer@jimcramer

We must NOT let our companies use these Chinese models to save a few bucks. OpenAI and Anthropic are correct. This is vital national security. Please read Bing West's just released Cat 5. I respect the Chinese people greatly but these companies are run by the PLA for heaven's sakes.

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Chamath Palihapitiya
The leading AI has already forked into two options. A: Closed source American that costs $26-56 per 1MM tokens. B: Open weight Chinese that costs $0.50-1 per 1MM tokens. If you force American companies to spend 50-100x more than their competitors abroad here is what will happen: 1. American companies spending 50-100x will at some point become financially impaired and the stock market will crater. This is the equivalent of the US Government saying we can only buy oil at $800/barrel even when the free market sells the same oil for $80/barrel. 2. In the short term, the revenues of the Closed American Labs may remain hi. But then their revenue will also crater because their customers in the US will be impaired and their customers abroad will have already flipped to cheaper solutions. This would be a terribly self-defeating form of intervention if it were to happen.
MTS@MTSlive

SITUATION BREWING: The Trump administration is considering restricting cutting-edge Chinese AI models, with momentum reviving after the launch of Kimi K3, per Axios.

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Jack Chan
Jack Chan@jackchan_x·
Orny Adams told Jerry Seinfeld he felt like a failure. He had spent a decade doing comedy while his high-school friends got rich on Wall Street. Jerry kept asking, “What?” He couldn’t understand what their money had to do with Orny’s comedy. Later, Jerry shared his own measure of success: “Are you still working?” They were using different scoreboards. Jerry measured what he could control: whether he was still doing the work. Orny measured what other people had to give him: fame, recognition, and status. The more your scoreboard depends on other people, the less control you have over your own decisions. Choose a scoreboard you can control—then let the evidence tell you when to change direction.
Jeremy Giffon@jeremygiffon

x.com/i/article/2079…

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a16z
a16z@a16z·
Amjad Masad on Going Direct & Founder Storytelling Replit CEO Amjad Masad joins a16z's Erik Torenberg on how he went from crippling stage fright to one of the most famous CEOs on X, which platforms actually matter and why, and what founders get wrong about building in public. 00:50 Memeing a dream into reality 03:02 Conquering stage fright 04:00 Make your mistakes early 05:23 Building in public 06:41 The authenticity edge: Elon, Zuck, Trump 08:42 Become uncancellable 10:47 Advice for Anthropic and OpenAI 15:09 The day AI deleted prod 17:39 Who should go direct? 19:37 X vs Instagram vs YouTube 22:52 Go direct role models @amasad @eriktorenberg
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Jack Chan
Jack Chan@jackchan_x·
When Replit Agent misunderstood a task and deleted SaaStr founder Jason Lemkin’s production database, Replit had two frames to choose from: “We have backups. The user can recover.” Or: “We gave the AI too much access.” The first frame defended the product. The second protected trust. Screenshots were already spreading, and Lemkin didn’t know how to restore the backup. So Amjad Masad opened with: “We messed up.” He admitted Replit lacked dev/prod separation, then shipped default isolation, production read-only access, and better recovery controls within two days. The response spread positively—and Lemkin kept using Replit Agent. Decision lesson: frame the problem around what you control, own the trust gap, and make the fix visible.
a16z@a16z

Amjad Masad on Going Direct & Founder Storytelling Replit CEO Amjad Masad joins a16z's Erik Torenberg on how he went from crippling stage fright to one of the most famous CEOs on X, which platforms actually matter and why, and what founders get wrong about building in public. 00:50 Memeing a dream into reality 03:02 Conquering stage fright 04:00 Make your mistakes early 05:23 Building in public 06:41 The authenticity edge: Elon, Zuck, Trump 08:42 Become uncancellable 10:47 Advice for Anthropic and OpenAI 15:09 The day AI deleted prod 17:39 Who should go direct? 19:37 X vs Instagram vs YouTube 22:52 Go direct role models @amasad @eriktorenberg

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Dom Cooke
Dom Cooke@domcooke·
My latest profile, which could have been titled Maxxed Out. What it’s like on the frontier of AI with @saranormous and her team at Conviction, who are very much in the thick of it.
Colossus@colossusmag

In 2018, Sarah Guo became the youngest general partner in Greylock's 60-year history. She was 28. Four years later, she quit to launch Conviction, a firm staked entirely on AI. Before ChatGPT shipped, she seeded Baseten and Harvey; each is now worth over $11 billion. In Conviction's first year, she wrote early checks into Sierra, Cognition, and Mistral; those three companies are now worth, together, $54 billion. Andrej Karpathy worked out of Conviction's office until Anthropic hired him in May. Guo has been close to Jensen Huang for over a decade. Her first two calls after starting the firm were to Sam Altman and Nat Friedman. And yet the investor closest to the AI frontier is betting against its biggest companies. The two big frontier labs, worth close to a trillion dollars apiece, no longer just want to build the models. They also want to build every product and company on top of them, leaving nothing for anyone else. The market is paying as though they might succeed. Of the $300 billion in venture capital deployed in the first quarter of 2026, the biggest quarter in the history of the trade, 65% went to only four companies: Anthropic, OpenAI, xAI, and Waymo. Guo is betting the labs can't build everything, and she spends her days making sure of it. She won Harvey its first client. She flew across the country to take a single Baseten candidate to a four-hour lunch. On one wedding anniversary, she spent the whole weekend on back-to-back calls, keeping two founders on the line so they couldn't speak to rival firms. Twice a year, she flies the world's brightest young founders to San Francisco and inducts them into the fight. In the months @domcooke spent reporting this piece, @saranormous had her fourth child, walked the Met Gala in 45 pounds of chainmail, and still answered her founders' texts within minutes. Guo's parents arrived from China in 1987 with $50, built a company, and took it public at $1.2 billion. Then it went bankrupt. Guo grew up inside that startup. She built its first website, did her homework in a cubicle, and slept over for bug bashes. She loved it. If two labs build everything, no one gets to do that again. Welcome to Sarah's Wager. Read it below.

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Jack Chan
Jack Chan@jackchan_x·
@colossusmag she has 3 persuasion power tools x.com/jackchan_x/sta…
Jack Chan@jackchan_x

Sarah @saranormous has incredible energy and this article profiles three powerful persuasion techniques—without slides or a hard sell: 1. Raise the other person’s standard A candidate was choosing between Conviction and OpenAI. Guo didn’t say, “Join us.” She said the OpenAI role wasn’t ambitious enough for her. The move: Don’t argue that your option is better. Ask which option matches the person they want to become. 2. Combine social proof with speed To win the Remotion deal, Guo spent the weekend finding people who could vouch for her while staying in constant contact with the founders. The move: Let trusted people establish credibility, then build momentum through responsiveness. 3. Turn one big decision into small experiences Mike Vernal wasn’t looking for a job. Guo gave him office access, invited him to evaluate startups, mentor founders, and eventually lead a deal. He joined 18 months later. The move: Don’t force an immediate commitment. Let people experience the future before choosing it. Great persuasion isn’t repeating how good you are. It’s changing the frame, providing proof, and making the next step easier.

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Colossus
Colossus@colossusmag·
In 2018, Sarah Guo became the youngest general partner in Greylock's 60-year history. She was 28. Four years later, she quit to launch Conviction, a firm staked entirely on AI. Before ChatGPT shipped, she seeded Baseten and Harvey; each is now worth over $11 billion. In Conviction's first year, she wrote early checks into Sierra, Cognition, and Mistral; those three companies are now worth, together, $54 billion. Andrej Karpathy worked out of Conviction's office until Anthropic hired him in May. Guo has been close to Jensen Huang for over a decade. Her first two calls after starting the firm were to Sam Altman and Nat Friedman. And yet the investor closest to the AI frontier is betting against its biggest companies. The two big frontier labs, worth close to a trillion dollars apiece, no longer just want to build the models. They also want to build every product and company on top of them, leaving nothing for anyone else. The market is paying as though they might succeed. Of the $300 billion in venture capital deployed in the first quarter of 2026, the biggest quarter in the history of the trade, 65% went to only four companies: Anthropic, OpenAI, xAI, and Waymo. Guo is betting the labs can't build everything, and she spends her days making sure of it. She won Harvey its first client. She flew across the country to take a single Baseten candidate to a four-hour lunch. On one wedding anniversary, she spent the whole weekend on back-to-back calls, keeping two founders on the line so they couldn't speak to rival firms. Twice a year, she flies the world's brightest young founders to San Francisco and inducts them into the fight. In the months @domcooke spent reporting this piece, @saranormous had her fourth child, walked the Met Gala in 45 pounds of chainmail, and still answered her founders' texts within minutes. Guo's parents arrived from China in 1987 with $50, built a company, and took it public at $1.2 billion. Then it went bankrupt. Guo grew up inside that startup. She built its first website, did her homework in a cubicle, and slept over for bug bashes. She loved it. If two labs build everything, no one gets to do that again. Welcome to Sarah's Wager. Read it below.
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Jack Chan
Jack Chan@jackchan_x·
Sarah @saranormous has incredible energy and this article profiles three powerful persuasion techniques—without slides or a hard sell: 1. Raise the other person’s standard A candidate was choosing between Conviction and OpenAI. Guo didn’t say, “Join us.” She said the OpenAI role wasn’t ambitious enough for her. The move: Don’t argue that your option is better. Ask which option matches the person they want to become. 2. Combine social proof with speed To win the Remotion deal, Guo spent the weekend finding people who could vouch for her while staying in constant contact with the founders. The move: Let trusted people establish credibility, then build momentum through responsiveness. 3. Turn one big decision into small experiences Mike Vernal wasn’t looking for a job. Guo gave him office access, invited him to evaluate startups, mentor founders, and eventually lead a deal. He joined 18 months later. The move: Don’t force an immediate commitment. Let people experience the future before choosing it. Great persuasion isn’t repeating how good you are. It’s changing the frame, providing proof, and making the next step easier.
Bret Taylor@btaylor

Sarah @saranormous is one of the most intense, engaged, and operationally excellent investors I have worked with — and every founder I have met who has worked with her feels the same way. Truly one of a kind colossus.com/article/sarah-…

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Jack Chan
Jack Chan@jackchan_x·
Mark Zuckerberg was considering firing his executive team for the second time. He asked Ben Horowitz, “Would the board be nervous?” Horowitz replied, “That’s not even the question,” then kept digging into why Facebook’s traffic had flattened. This is classic upstream thinking: don’t react at the level where the symptom appears. Trace the causal chain until you find the system producing it. A better diagnosis doesn’t just improve the answer. It can change the decision.
Jack Chan tweet media
Startup Archive@StartupArchive_

Ben Horowitz tells the story of Mark Zuckerberg firing his executive team for the second time “The very first conversation I had with Zuck was I think in 2007,” a16z co-founder Ben Horowitz begins. “At that time, Facebook traffic had flattened and the executive staff that he had was trying to run a coup to force him to sell it to Yahoo. So they were leaking all this stuff to Valleywag and Valleywag was calling for Zuck to be fired and that whole stupidness.” A young Mark Zuckerberg asked Ben a question: “If I fired my executive team for the second time, would the board be nervous?” Ben replied: “Well, that’s not even the question Mark because if you’re asking that question you know you have to do it. You can’t succeed with them so whether or not you can succeed without them is still at least a question mark. But let’s talk about why they’re doing this. Why has traffic been flat?” Mark explained that after doubling the size of the engineering team from 400 engineers to 800, a lot of the new engineers were directly querying the database rather than using the API layer which broke the system and was resulting in performance issues (e.g. it taking 10 seconds to log into the app). “Well how do you train these guys?” Ben asked. “Train these guys?” Mark replied confused. Ben explained: “Zuck, when you’re 10 people there’s no knowledge in the company. Everyone who comes on just jumps in and starts working. But when you get to 800 people, you have a lot of knowledge that’s in your company about how the product works and how you check in code. You actually have to teach people that.” Mark would go on to create Facebook’s world-class 2-month bootcamp that every engineer who joins the company has to go through. “He’s a phenomenal student of management,” Ben remarks. “And the [leaders] who don’t truly understand people don’t turn out to be good CEOs. They don’t get to that level. You can make fun of Larry Page or Elon or Zuck, but they are very smart about people. All three of them.” Source: @myfirstmilpod (Dec 2025)

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Jesus
Jesus@JesusMalaga·
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Jack Chan
Jack Chan@jackchan_x·
“Conviction” is too vague to explain Nvidia. Five moments in Stephen Witt’s The Thinking Machine show what actually changed the decisions. 1. Riva 128: judge risk against the alternative Nvidia skipped the physical prototype and sent NV3 into production. “It was fifty-fifty, but we were going out of business anyway.” This was not a general argument for boldness. Bankruptcy had already removed the safer option. 2. CUDA: count the risk of doing nothing Wall Street thought CUDA had negative value. Huang’s answer: “There was a risk in shipping CUDA with every card, but there was also a risk in not doing it.” The status quo was also a bet. A smaller company could use scientific computing to do to Nvidia what Nvidia had done to Silicon Graphics. 3. AlexNet: let the evidence carry the presentation Alex Krizhevsky ended his nervous, poorly delivered presentation with: “That’s it. That’s all I have.” But AlexNet scored above 80% on ImageNet, against roughly 70% for the previous state of the art. After checking for contamination and calculation errors, the field had to update. The presentation was weak. The benchmark was inspectable. 4. Shoquist: replace “impossible” with a cost schedule Normal packaging: Three weeks. $8 per chip. Fastest theoretical version: 36 hours. $1,000 per chip. Huang looked at the schedule and said: “That’s the right answer.” He was not choosing the faster option. He was choosing an answer that exposed the price. 5. O.I.A.L.O.: change identity after the proof arrives CUDA existed. AlexNet had worked. Bryan Catanzaro had reproduced Google’s CPU experiment with 12 GPUs. Then Huang redirected the company. “By Monday morning, we were an AI company. Literally, it was that fast.” The language did not create the evidence. It told the organization what the evidence now required. None of these moments turned on a better motivational speech. Each made a hidden trade-off visible enough to act on. From the pipeline: Stephen Witt, The Thinking Machine. Filed under: decisions under pressure $NVDA
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Jack Chan
Jack Chan@jackchan_x·
@theallinpod @Jason @PGelsinger @antonosika Intel will be an evergreen case study of tech history. Intel’s story is not simply that it missed the iPhone, GPUs or the foundry market. It is a story about technical leadership, capital allocation and institutional patience. x.com/jackchan_x/sta…
Jack Chan@jackchan_x

Everybody knows the first Andy Grove story. In 1985, Intel’s memory business was being crushed by Japanese manufacturers. Management kept debating how to save it: build a larger factory, develop better technology, find a specialized niche. Every proposal carried the same assumption: Intel had to remain a memory company. Grove asked Gordon Moore: “If we got kicked out and the board brought in a new CEO, what would he do?” Moore answered immediately: “He would get us out of memories.” Grove replied: “Why shouldn’t you and I walk out the door, come back, and do it ourselves?” Intel exited memory and made microprocessors its core business. That story is famous. Almost nobody knows the second Grove story. Twelve years later, Intel was already the dominant microprocessor company. The threat was now coming from below. AMD and Cyrix were selling cheaper, less powerful chips for low-cost computers. Grove invited Clayton Christensen to Intel to explain what disruptive innovation meant for the company. When Christensen arrived, Grove told him that his schedule had changed. He had ten minutes. “Tell us what your model means for Intel.” Christensen refused. He knew the theory of disruption. He did not know Intel well enough to prescribe its processor strategy. Instead, he told Grove about steel. Steel minimills had entered at the bottom of the market, producing rebar: cheap, undifferentiated and low-margin. The integrated steel companies were happy to leave. Each time they abandoned a low-margin segment, their profitability improved. Then the minimills improved. They moved from rebar into bars and rods, then structural steel, then sheet steel. The incumbents kept retreating toward more attractive customers until there was nowhere left to retreat. Christensen stopped there. Grove completed the analogy himself. Low-cost processors were Intel’s rebar. AMD and Cyrix were the minimills. If Intel abandoned the low end because the margins were unattractive, its competitors would gain volume, revenue and technical experience there. Eventually, they could move upmarket and attack Intel’s core. Intel went on to make a serious push into low-cost PCs with Celeron. “Rebar” became internal shorthand for the threat from below. The interesting part is not that Christensen was a good storyteller. It is that he refused to play the role Grove initially gave him: the outside expert who tells the CEO what to do. A direct recommendation—“Intel should launch a cheaper processor”—would have been easy to reject. Do you understand our costs? Our architecture? Our channels? Our roadmap? The conversation would have become a test of Christensen’s semiconductor expertise. He would have lost, correctly. So he moved the problem into an industry where Grove had nothing to defend. There was no Intel strategy in the steel story. No existing decision to justify. No executive identity at stake. Grove could examine the mechanism before confronting its implications. Christensen also chose a story with causal structure, not a decorative analogy: Enter through a low-margin segment. Let the incumbent retreat. Use the foothold to improve. Move upmarket. Repeat. Then he withheld the final mapping. That matters. Grove did not receive a recommendation from an academic. He reached a conclusion about Intel using a model he now understood. The judgment remained his. I’d call this move The Unfinished Analogy: Explain a mechanism in a distant domain. Make the causal sequence visible. Stop before applying it to the listener’s situation. Let them finish. One caveat: Christensen did not invent Celeron in a ten-minute conversation. Intel was already paying attention to low-cost PCs. What he gave Grove was a sharper model of the threat—and language that could travel through the organization. The mark of a useful strategic conversation is not that the adviser delivers a brilliant answer. It is that the decision-maker interrupts and starts explaining the answer back. From the book What is Your Problem by Thomas Wedell-Wedellsborg. Filed under Unfinished Analogy.

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The All-In Podcast
The All-In Podcast@theallinpod·
LIVE from Paris: Former Intel CEO on What Went Wrong + What's Next, Lovable CEO on the Future of Vibe Coding @Jason sits down with @PGelsinger and @antonosika (0:00) Former $INTC CEO Pat Gelsinger joins Jason! (1:41) What Went Wrong at Intel (15:19) Why a Taiwan Blockade Would Cripple the US Economy (25:00) Lovable's Anton Osika: One Million New Apps a Week (33:38) How Lovable is Bringing Down Builder Costs --------------------------- Thanks to our partners for making this possible! @airwallex is a leading global payments and financial platform for modern businesses, offering trusted solutions to manage everything from business accounts, payments, treasury, and spend management to embedded finance. airwallex.com/allin @PLAUDAI - If your work depends on conversations — meetings, deal flow, interviews, customer calls — Plaud helps you capture and organize everything with highly accurate AI-generated notes that are not just simple summaries, but also highlight pain points, key decisions, next steps, and customizable summary templates. Check out Plaud at Plaud.ai/allin and use code ALLIN for up to 20% off! Which is also available on Amazon: amzn.to/43URLff (Code: ALLIN20X) And a special thank you to @RaiseSummit for hosting us in Paris
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