We instituted an official AI Writing Policy at @Clay. Massive thanks to @sophiebits on our engineering team who wrote this. Originally this was just for eng, but other teams found it so helpful we expanded it company-wide. Here are the four guiding principles: 1. You must stand behind every idea and sentence It is your responsibility to make sure that the entire document is representative of your own thoughts before you share it. 2. Writing is thinking Spending time on the writing process teaches you more about your topic. If you circumvent this process, you will walk away with a poorer understanding of the subject matter. 3. More time should be spent writing a document than consuming it If you generate a document from a short prompt then ask your readers to go through the longer output, you are disrespecting their time. They can talk to ChatGPT themselves if they want to. 4. Longer is not better AI makes it easy to generate long docs, and it loves padding them with sentences that say nothing. If you're producing docs from a short prompt, consider just sharing the prompt. You can read (and borrow) the full policy below. Hopefully we all communicate a bit more clearly now :)

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There's a flavor of AI anxiety I run into all the time: companies feel like, no matter what they do, they're falling behind the AI curve, and the only way to catch up is to adopt another tool. But the number of AI tools has nothing to do with actually deriving any value from AI. This reminds me of something my dad said when I was offered a research job in college at MIT’s AI lab, and I called him to ask what he thought. He immediately said, “Why would you do that? AI is where all good technology goes to die.” His point was simple: “The moment something works, we stop calling it AI.” That still feels true. Ask a company which AI they use, and you'll hear about the newest model or whatever a vendor demoed last quarter. But the AI they actually depend on is the invisible kind. Think about your email inbox. It's been using machine learning for 20 years to sort out what’s spam, yet nobody thinks, "I'm using AI right now" when they skim their junk folder. The most effective AI doesn't announce itself. It's probably already running in your business, and you're just calling it something else.

The hateful campaign being run by Daniel Bilzerian in Florida is blatantly antisemitic and shocks the conscience. People of goodwill regardless of political affiliation must reject his vile attacks on the Jewish community. This cannot and will not stand.

This Meta campaign is a case study in strategic reframing, with 4 major examples: 1) REFRAMES THE AI RACE FROM “who builds it the best” TO “who distributes it to the most people”: Instead of fighting over who has the most capable models, Meta is framing the race as being won by distribution. With open models, free products, consumer dna, and 3.5 bn users, Meta is best positioned here 2) REFRAMES COMPETITORS FROM “holding the technical lead” TO “holding too much concentrated power”: Classic populist stance, with the insight that hostility to AI is largely hostility to being subjugated by an overlord class of AI perma-elites. The frontier labs’ biggest advantage (lead in capabilities) becomes a liability, while Meta’s huge user base is used to reposition Meta too powerful in its own right to being a check on the power of the frontier labs (the perfect foil) 3) REFRAMES SAFETY DEBATE FROM “more vs less safe” TO “optimists vs doomers”: OpenAI and Anthropic have spent years focusing on AI risks, and many people see this as noble and responsible. By equating safety focus to doomerism, Meta repositions itself from being less safe to being more optimistic about the future, while making competitors look like tiresome hypocrites 4) REFRAMING META’S PORTFOLIO FROM “unfocused and capricious” TO “a future for everyone”: In jumping between crypto, metaverse, AI, hardware, Meta has been accused of lack of focus and trying to do everything. But now they can position these motley initiatives as being unified by the coherent theme that Meta technology is “for everyone.” It’s not ADHD, it’s a mission to put frontier technologies into the hands of ordinary people In the process, they’re also tapping into many parts of the zeitgeist: distrust of concentrated power, support for open models, permanent underclass anxiety, doomer fatigue But Meta’s key strategic move here is to change the criteria for the AI race. Under the old criteria (capabilities of frontier models) Meta was seen as behind. Under these new criteria (widespread universal distribution of technology), Meta might actually have the strongest position in the industry

🚨🗣️New: Lionel Messi on why he started crying after receiving the silver medal despite the World Cup final defeat to Spain: “I wasn’t just thinking about losing a final. I was thinking about everything that brought me here… every sacrifice, every difficult moment, every time people doubted us, and every time this shirt gave me the strength to continue. “When I heard the Argentina fans chanting my name after the defeat, that was the moment that broke me. We had just lost the biggest game, but they didn’t turn their backs on us. They didn’t see a silver medal… they saw the journey. They saw the heart, the fight, and the pride we gave them. “I looked into the stands and I saw families who travelled thousands of miles, people who dreamed with us, people who cried with us. That love is bigger than any trophy. Football gives you many victories, but moments like that remind you why you play. “I cried because I felt the weight of the shirt, the love of my country, and the responsibility of wearing this badge. A medal can be silver, but the memories, the connection with the people, and the pride of representing Argentina… those things are priceless. “Sometimes football gives you the perfect ending, and sometimes it gives you a painful chapter. But I learned that greatness is not only measured by lifting trophies. It is also measured by how you stand when your heart is broken. “And hearing Argentina sing my name after losing a World Cup final… that was a victory of another kind. I will carry that moment with me forever.” —🎙️Mixed Zone

As I wrote this, I saw X go into meltdown over tokens. You've seen the headlines: “Uber blows yearly AI budget in just one quarter.” “Meta employee burns 281 billion tokens in April.” But, the problem isn't spending. Spending works. Since 2023, the top quartile of our AI spenders doubled their revenue. The bottom quartile? Flat. It's blind spending. We don’t know which spend worked. A sales team has qualified leads. A support team has resolved conversations. These are units you can measure against. All a token tells you is the meter ran, not whether the work was worth it or not. Finance says, “half the budget,” engineering says, “double it” and you don’t know who’s right because there is no shared language of value. There’s no attribution, and no attribution means no allocation. For example, right now, all work, no matter the size or shape, defaults to frontier models. But meeting summaries and calendar updates don’t require GPT-5.5 Pro. In isolation this seems trivial, but re-route just 10% of a $10M AI bill from frontier to GPT-4 level intelligence you’ve saved nearly one million dollars. This sounds like a made-up stat — it’s not. It truly is that much cheaper. This is the future of finance: not blindly rubber-stamping or rejecting AI spend, but allocating it with the same rigor companies apply to headcount.

Brian still spends over two hours a day on recruiting and personally hires the top 200 people at Airbnb. I loved this idea of being in the flow of talent to find the best people: "Don't do searches. Build pipelines. I try to map out all the best people in the Valley. So let's say I need to hire really good engineers. I don't do searches. I just informationally meet the best engineers in the world. Every meeting, the job is to get the next meeting, meet someone else. The mistake people make when they hire. They go, "I need to hire a blank." So they hire a search firm. They give you 50 profiles, and you pick the best one. That is the wrong way to do it. The best way to do it is pipeline recruiting. You're constantly recruiting, you're constantly meeting people. in advance of searches. And all of it is referral based. The two ways to find out if people are good – is to start with the results and work backwards to the people. Find an ad you like and figure out who made that ad. Start with the results. Work backwards to people. Don't start with the resume. The other thing to do is just keep asking people to build your Rolodex. The moment I find somebody that's really good, I ask them who all the best people they know are. And I build these little mafias and they tell you who the other good people are. I am the co-hiring manager for the top 200 people in the company. This is very radical. A lot of CEOs think it's their job to hire their executive team, and their executive team hires their team. I think that is fatal. You always want to be marrying up, hiring people of the future. It should be like we're reaching. If you can hire them without my help, we're not reaching far enough. You want to hire the very best person you can."

How to write an exceptional cold email. @MichelleKhare

New York City, meet the hero cops who apprehended two individuals charged with ISIS-inspired terrorism last week. Our entire city owes these officers a debt of gratitude. They are a reminder that the safety and security of New York City rests on the judgment and courage of the men and women of the NYPD. On Saturday, March 7, that was on full display. When two IEDs were ignited in the middle of a crowded protest, these officers didn’t step back. They stepped forward without hesitation and without regard for their own safety. They acted on instinct and training to protect people they did not know and would likely never meet. Moments like that remind us what policing truly requires.

I've used em-dashes my whole life — they add rhythm and grace to writing. But now they're an AI tell. Can we get a grandfather clause for those of us who were fluent in em-dashes before ChatGPT launched in November 2022?

I’m in love with this sentence: “The degree to which a person can grow is directly proportional to the amount of truth he can accept about himself without running away.”

Marc Andreessen just dropped ~105 mins on Lenny's Podcast covering AI, jobs, careers, and why everyone is panicking about the wrong thing. Just the clearest macro framework I've heard on where AI actually lands. My notes: 𝟭. 𝗔𝗜 𝗶𝘀 𝗮𝗿𝗿𝗶𝘃𝗶𝗻𝗴 𝗮𝘁 𝘁𝗵𝗲 𝗲𝘅𝗮𝗰𝘁 𝗺𝗼𝗺𝗲𝗻𝘁 𝗵𝘂𝗺𝗮𝗻𝗶𝘁𝘆 𝗻𝗲𝗲𝗱𝘀 𝗶𝘁. US productivity growth has been running at half the rate of the 1940-1970 era and a third the rate of 1870-1940. The global population is declining below replacement in dozens of countries, including China. Without AI, we would be panicking about economies shrinking from depopulation, not job loss. The timing is almost miraculous. This is what Andreessen means when he says the real boom has not started yet. We have been in a 50-year productivity drought, and most people do not even realize it. 𝟮. 𝗔𝗜 𝗶𝘀 𝘁𝗵𝗲 𝗽𝗵𝗶𝗹𝗼𝘀𝗼𝗽𝗵𝗲𝗿'𝘀 𝘀𝘁𝗼𝗻𝗲. Isaac Newton spent decades trying to transmute lead into gold and never succeeded. AI does something more powerful: it converts sand (silicon) into thought. The most common material in the world is the rarest output. This one metaphor reframes the entire AI conversation. You do not have a job loss problem. You have a philosopher's stone sitting on your desk that you are not using enough. 𝟯. 𝗔𝗜 𝗺𝗮𝗸𝗲𝘀 𝗴𝗼𝗼𝗱 𝗽𝗲𝗼𝗽𝗹𝗲 𝘃𝗲𝗿𝘆 𝗴𝗼𝗼𝗱, 𝗮𝗻𝗱 𝘃𝗲𝗿𝘆 𝗴𝗼𝗼𝗱 𝗽𝗲𝗼𝗽𝗹𝗲 𝘀𝗽𝗲𝗰𝘁𝗮𝗰𝘂𝗹𝗮𝗿𝗹𝘆 𝗴𝗿𝗲𝗮𝘁. The best coders right now are not reporting 2x productivity. They are reporting 10x. The gap between "pretty good with AI" and "elite with AI" is widening, not narrowing. This is the most important signal for career planning right now. If you are just using AI to do the same job slightly faster, you are leaving the real leverage on the table. 𝟰. 𝗧𝗵𝗲𝗿𝗲'𝘀 𝗮 𝗠𝗲𝘅𝗶𝗰𝗮𝗻 𝘀𝘁𝗮𝗻𝗱𝗼𝗳𝗳 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝗣𝗠𝘀, 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀, 𝗮𝗻𝗱 𝗱𝗲𝘀𝗶𝗴𝗻𝗲𝗿𝘀. Every engineer now thinks they can be a PM and designer. Every PM thinks they can code and design. Every designer knows they can do both. And they are all correct, because AI enables each role to absorb the tasks of the other two. I have seen this firsthand in the investing world. The analyst who can build models and write narratives is 5x more valuable than someone who can do only one. The same convergence is happening in the product. 𝟱. 𝗙𝗼𝗿𝗴𝗲𝘁 𝗧-𝘀𝗵𝗮𝗽𝗲𝗱. 𝗕𝘂𝗶𝗹𝗱 𝗮𝗻 𝗘-𝘀𝗵𝗮𝗽𝗲𝗱 𝗰𝗮𝗿𝗲𝗲𝗿. Scott Adams could not have created Dilbert by being the world's best cartoonist or the world's best business mind. He needed both. The additive effect of two skills is more than double. Three skills are more than triple. Larry Summers puts it differently: don't be fungible. The person who can code, design, and ship a product is no longer a unicorn. They are the new baseline for "extremely valuable." If you are only one of those three things, you are increasingly replaceable. 𝟲. 𝗝𝗼𝗯𝘀 𝗮𝗿𝗲 𝗯𝘂𝗻𝗱𝗹𝗲𝘀 𝗼𝗳 𝘁𝗮𝘀𝗸𝘀. 𝗧𝗮𝘀𝗸𝘀 𝗰𝗵𝗮𝗻𝗴𝗲. 𝗝𝗼𝗯𝘀 𝗽𝗲𝗿𝘀𝗶𝘀𝘁. Executives never typed their own emails in the 1970s. Secretaries printed incoming emails and hand-delivered them. Both roles survived the transition, just with different task sets. The same will happen with AI and coding, PM work, and design. Everyone obsessing over "will my job disappear" is asking the wrong question. The right question is: which tasks in my job are about to rotate, and am I ready to pick up the new ones? 𝟳. 𝗔𝗜 𝗰𝗼𝗱𝗶𝗻𝗴 𝗶𝘀 𝗷𝘂𝘀𝘁 𝘁𝗵𝗲 𝗻𝗲𝘅𝘁 𝗮𝗯𝘀𝘁𝗿𝗮𝗰𝘁𝗶𝗼𝗻 𝗹𝗮𝘆𝗲𝗿. We went from human calculators to machine code to assembly to C to scripting languages. Each layer was dismissed by the previous generation. Each time, the new layer won, and total coding employment grew. AI coding is the same pattern, not a rupture. The Perl programmers of 2005, laughing at JavaScript, are the C programmers of 1995, laughing at scripting. History rhymes, and it always rewards the people who adopt the next abstraction first. 𝟴. 𝗔𝗜 𝘁𝘂𝘁𝗼𝗿𝗶𝗻𝗴 𝗱𝗲𝗺𝗼𝗰𝗿𝗮𝘁𝗶𝘇𝗲𝘀 𝗲𝗹𝗶𝘁𝗲 𝗲𝗱𝘂𝗰𝗮𝘁𝗶𝗼𝗻. One-on-one tutoring is the only method proven to move a student from the 50th to the 99th percentile (Bloom's two sigma effect). It used to require being born into royalty. Alexander the Great was tutored by Aristotle. Now, any kid with a phone can access the same quality of personalized instruction. This is the most under-discussed consequence of AI. Every parent reading this should be supplementing their kid's education with structured AI tutoring right now. Not next year. Now. 𝟵. 𝗣𝗲𝘁𝗲𝗿 𝗧𝗵𝗶𝗲𝗹 𝘄𝗮𝘀 𝗺𝗼𝗿𝗲 𝗿𝗶𝗴𝗵𝘁 𝘁𝗵𝗮𝗻 𝗔𝗻𝗱𝗿𝗲𝗲𝘀𝘀𝗲𝗻 𝗼𝗿𝗶𝗴𝗶𝗻𝗮𝗹𝗹𝘆 𝗮𝗱𝗺𝗶𝘁𝘁𝗲𝗱. Progress in bits masked stagnation in atoms. The built world is barely different from 50 years ago. Same bridges from the 1930s, same dams from the 1910s. Cartels, monopolies, unions, and regulations prevent the rate of change that people had 100 years ago. This is also why AI will not transform everything overnight. Institutional sclerosis is real. Healthcare alone could take a generation. If you are building in atoms, budget for a war of attrition, not a blitzkrieg. 𝟭𝟬. 𝗠𝗼𝗮𝘁𝘀 𝗶𝗻 𝗔𝗜 𝗮𝗿𝗲 𝗴𝗲𝗻𝘂𝗶𝗻𝗲𝗹𝘆 𝘂𝗻𝗸𝗻𝗼𝘄𝗻. Within a year of ChatGPT's launch, five American companies, five Chinese companies, and open-source all had roughly equivalent models. DeepSeek emerged from a hedge fund in China and basically replicated the American labs' work. The smartest AI insiders privately admit there aren't many real secrets among the big labs. This is the most honest take I have heard from a top-tier VC. No one knows if the value accrues to models, apps, or infrastructure. Anyone who tells you otherwise is selling you certainty they do not have. 𝟭𝟭. 𝗔𝗜 𝗜𝗤 𝘄𝗶𝗹𝗹 𝗯𝗹𝗼𝘄 𝗽𝗮𝘀𝘁 𝗵𝘂𝗺𝗮𝗻 𝗹𝗶𝗺𝗶𝘁𝘀. Human IQ caps around 160 because of biology. Current AI models test around 130-140. There is no theoretical ceiling stopping AI from reaching 200, 250, or 300. The concept of AGI as a "human equivalent" will be a footnote because AI will race past that threshold. This is the frame that makes the "will AI take my job" debate feel small. We are not building a replacement for human thought. We are building something that will be better than the best human thought has ever been. 𝟭𝟮. 𝗧𝗵𝗲 𝗯𝗲𝘀𝘁 𝗳𝗼𝘂𝗻𝗱𝗲𝗿𝘀 𝗮𝗿𝗲 𝗿𝗲𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝘄𝗵𝗮𝘁 𝗮 𝗰𝗼𝗺𝗽𝗮𝗻𝘆 𝗲𝘃𝗲𝗻 𝗶𝘀. Layer one: AI redefines products. Layer two: AI redefines jobs within companies. Layer three, which has not dropped yet: AI redefines the very concept of having a company. The holy grail is the one-person, billion-dollar outcome, and the best founders are chasing it. Satoshi did it with Bitcoin. Instagram and WhatsApp came close with tiny teams. The question is no longer if this is possible with software. The question is how many of these we will see in the next five years. AI is the philosopher's stone. The question is whether you pick it up. The full podcast is worth your time. Link in replies.