@WillManidis 100p my personal favorite is "the best way way to compete with an incumbent is to build something at the intersection of two VPs" they hate each other more than they could ever love the customer courtesy of @stevesi

Ben Horowitz and Erik Torenberg with Nas, Steve Stoute, and Grandmaster Caz on the Paid in Full Foundation Caz penned rhymes that became Rapper's Delight. Even with his name in them, he got nothing. The Paid in Full foundation honors the people who created hip-hop by making sure they're "paid in full," financially, spiritually, and culturally. As Nas points out, money alone reads as a handout. So the grant comes attached to the honor. Each year at the foundation's Hip Hop Grandmaster Awards, pioneers are celebrated by the artists they inspired. Caz ultimately received a five-year grant, bought a house, and left the projects. In his words: "The thing is the honor. The thing is putting you in the light that you deserve to be in." Ben says he bought Paid in Full (the album) for $10 and got at least $5 million worth of value out of the record, with no way to pay it back. The vision is paying the debt. Felicia Horowitz runs the foundation. She and Ben are matching donations 2.5 to 1, and every dollar goes to the artists. 0:00 Intro 1:40 The $10 album that was worth $5M to Ben 3:10 Nas: in hip-hop, you can't just give money 5:05 Why artists thought it was a scam 9:30 Caz wrote Rapper's Delight and got nothing 11:40 Roxanne Shanté: "I wanted the right award" 16:35 Dr. Dre asks to meet Kool Moe Dee and Slick Rick 18:50 Why the industry never did this 26:05 Ben: start with what's right, not what's possible 29:10 Inside the room: Grand Puba, George Clinton 32:15 The Quincy Jones Award 37:00 Scarface and Rakim talk writing for the first time 38:45 Caz: the grant got me out of the projects 43:45 What hip-hop did for Adidas, Hilfiger and Sprite 48:00 How Felicia saved Scarface's life 52:10 Quincy Jones stories, and Nas's jazz legend dad @Nas @SteveStoute @GrandmasterCaz @bhorowitz @eriktorenberg

An opportunity hiding in plain sight is that much of the Instagram audience seems to prefer the Muse visual interface to iMessage. Muse has done some things well, but in many ways it’s a v1 UI for the most powerful capability that’s been invented since ChatGPT. Somebody’s going to build a more opinionated design that’s counter-positioned against Muse / specialized for an audience in a way that’s hard to copy, and run away with a really valuable part of the market.

Comparative advantage, i.e., how agents discover / hire / diligence / pay other agents, is going to be a critical concept in this new world. - What are the “specialized” agents that will need different training / prompting / personality / capabilities that Muse doesn’t want to build? - Are there agents that can own and be paid for liability when taking risky actions? - Are there “luxury” agents that increase social capital? - You may want a neurotic agent for finance work and a creative agent for party planning, so even aside from intelligence / capabilities, specialization is needed to reconcile different “personalities.” What will be the key archetypes? - Will be is it more profitable to own an agent that executes known transactions or drives discovery through taste and personalization?

engineering is moving 10x faster. design review hasn't caught up. introducing Impeccable ..for PRs! a design review agent that simulates your users, uses your app, finds friction & slop. Impeccable’s design judgment in every PR. reply if you want in on the closed beta 👀

a16z's Anish Acharya says companies like Cursor and Decagon prove moats don't need to be designed, they can be discovered: "There's two important ideas. One is something @thejessezhang from Decagon said, which I love, and that is moats are most often discovered, not designed." "It's really easy, and I've done this as a founder, to get in your own head about needing a business plan that survives scrutiny from MBAs and VCs... And for that team, they just started shipping and it developed over time." "Another great example is Cursor. They were criticized a lot for not having a moat, but it turned out that initially being a high NPS, high DAU product was really good. And over time they captured all the reasoning traces, they trained their own models... We know how that story plays out." "The other idea is that we seem to have forgotten that none of the classic moats are based on how hard it is to make the software. Most of us aren't building self-driving cars. So it's network effects, scale advantages, brand effects, proprietary data, or what was historically called a cornered resource." "Every moat from five years ago generally is still a good moat. We just need founders that have ambition in those directions. We need more multiplayer products. We need consumer social. We need products that get dramatically better the more you use them." @illscience w/ @lennysan

Super fun hanging with @PaarupLuis at Connected Stack this morning, we covered: - How the best evals are customer outcomes - Agents need opportunities to make mistakes to do their best work, not excessive guardrails - Why custom models create out-performance in vertical markets

Today we're announcing @lightfld's $47M Series A led by @a16z to reimagine CRM as a world model of a business. For agents to do customer-facing work, they need to understand how your business actually works. Salesforce wasn't designed for this. It was built 20+ years ago for humans to update records. Layer agentic processes on top and you get low quality output, because the underlying data is incomplete and lacks the structure agents need. @lightfld updates itself from every interaction, forming a trustworthy model of your business for people and agents. Since launching last November, 5,000+ companies have signed up - and we're ripping out legacy CRM at mature organizations with hundreds of users. Companies on Lightfield grow faster than their competitors. It finds prospects that look like your best customers and books meetings with them. It captures commitments from every conversation and automates follow-ups. It diagnoses weaknesses in your funnel and learns from your best sellers to codify what works. The companies of the future will run on a business world model, not a Salesforce-era database. Our mission is to put that capability in the hands of every employee, and every agent, at every company.

The new moats are the same as the old moats Every few years, we fall in love with shiny new tech and forget the basic physics of consumer software. We’re doing it again with AI. The new moats aren't new at all. They're the exact same as the old moats: network effects, marketplaces, and platforms. Right now, consumer AI is booming. New agents like Instinct, Bot, and Tomo are dropping mind-blowing experiences. The underlying tech is incredible, but almost every product being built today shares the exact same challenge: They are completely single-player. Single-player products are 100% tied to value - and in this case mostly agent : model performance. If a competitor drops an agent tomorrow that books travel faster, tracks habits better, or handles life admin more reliably, everyone can switch overnight because leaving is easy and has nearly zero friction. Especially when it is so easy to onboard with just a new message. The legendary consumer tech giants didn't win because their underlying technology stayed marginally better forever. They won because of structural lock-in: Social Networks: You don't abandon WhatsApp for a prettier UI if your friends aren't there. Marketplaces: Airbnb, Doordash, and Uber hold supply and demand in a tight loop. Platforms: Apple and Android deliver you a complete device so you take advantage of the software on top of it (though this creates opportunities too) Novelty gets you initial distribution. Multi-user dynamics give you long-term retention. If your consumer AI product doesn't become exponentially more valuable to User A when User B joins, you don't have a moat, just a temporarily superior feature set. We are seeing this in the coding agents as people jump from tool to tool based on the best performance. But… all is not lost. There are huge opportunities here. Agents will get better when more of our friends are on them and can help us coordinate and communicate to do more together. Agents that help us improve and strengthen our habits can get better as we add friends and hold each other accountable. Data flywheels are great, but social and marketplace flywheels are what actually build enduring tech giants. It’s time to stop building isolated AI tools and start building the platforms where people connect, transact, and coordinate together.

My biggest takeaways from @illscience: 1. Company building will now be creating a series of loops. A coding loop turns a bug report into a fix and a low-risk production release in five minutes. Anish expects similar loops to spread from individuals to functions, business units, and eventually large parts of a company—from growth experiments to sales demos to legal and support. The key is to figure out what the agent doesn’t know and give it that context so it can complete the loop. 2. Moats are discovered, not designed. Anish uses Cursor as an example: it began as a high-engagement DAU product, then captured reasoning traces and trained its own models over time. A founder does not need a fully formed moat story on day one if the product has momentum, craft, and growing engagement. The classic moats—like network effects, scale advantages, brand, and proprietary data—still matter, but the small product decisions that create them often become visible only after the product is in the world. 3. We appear to be on the slow takeoff timeline. The case for fast takeoff always follows the same structure: everything up until now is OK, then something no one can articulate happens, then runaway acceleration. Anish doesn’t buy it. Model progress is real and faster than ever, but most problems aren’t intelligence-bound. A data center of PhDs doesn’t exponentially improve pizza supply chains. 4. The “permanent underclass” fears are a Silicon Valley dark fantasy. By almost every empirical measure, things have never been more distributed or opportunity-rich. Job postings for radiologists and programmers are at historic highs, despite years of “they’re cooked” predictions. And within the AI stack itself, rather than one winner-take-all platform, there are 20 credible players at every layer. 5. The biggest opportunity in consumer AI right now is “/loop make me happier” (not “/loop make me more productive”). Most people want to spend time, not save it. The biggest products in the world are entertainment and social, not productivity tools. Anish sees a spiritual hunger, particularly outside major urban centers where cultural institutions have thinned out. The opportunity: How do we feel more connected, more loved? How do we have fun? “We built a technology that extends our intellect and nothing to extend our soul.” 6. Three things have held consumer AI back, and all three are now improving. First, model costs were too high for free-to-use consumer products. Second, chat is a high-agency interface that works for Elon and Sam but not for the average consumer, who needs something between chat and TikTok. Third, the technology has been aimed almost entirely at productivity rather than connection and entertainment. The consumer moment is coming; Anish puts us at iPhone 2010, pre-Airbnb, pre-WhatsApp, pre-Uber. 7. Humans will remain critical for identifying the next opportunity. Agents are excellent at climbing to a local maximum, but then they plateau. They aren’t great at picking which hill to climb next. That’s where humans come in. Anish illustrates this with a chart he uses in conversations: agents hill-climb, then a human steps in to set the direction for the next climb, and the cycle repeats. 8. The most important attribute for teams is now ambition. Three years ago, Anish and his colleagues would pass on companies that seemed too crazy or complex. Today the opposite is true: an idea that’s too small isn’t worth engaging with. The firm’s internal posture with every founder: “We’re here to help you build the strongest form of your vision.” 9. There’s a big opportunity in creating very expensive consumer software. The old wisdom was that consumer products have to be free. Anish is taking the opposite position—that consumer discretionary spend is entirely up for grabs, and price is a measure of product-market fit. His product exercise for founders: what would the Birkin bag version of your product, at $1,000 or $10,000 a month, have to do to justify that price? 10. Become a model sommelier. Models are not interchangeable; Anish experiences different models as suited to different kinds of work. His way to learn their shape is to build something with every release, ideally using one or two low-stakes projects as a recurring test bed. His minimum heuristic is to ship once a week.

The "permanent underclass" is a dark fantasy Silicon Valley keeps telling itself. @illscience (GP at @a16z) points out that by almost every measure, things have never been better, and the job-loss story hasn’t played out as people thought. Radiologists have been “cooked” for 20 years and demand keeps rising. Open engineering roles are at all-time highs. And unlike the mobile era’s winner-take-all markets, every layer of the AI stack has ~20 strong competitors. We discuss: 🔸 Why you don’t need to be chronically online to keep up 🔸 The "/loop make me happier" opportunity in AI 🔸 Why every company is becoming a series of loops 🔸 Why moats are discovered, not designed 🔸 What's the Birkin bag version of your product? Listen now 👇

Sharing the deck we presented to our LPs last Friday with a snapshot of the broad market, AI applications, and consumer. Market Technology progress vs economic diffusion What if it isn't a bubble Moats are discovered, not designed Model (non)commoditization Apps Turning the intelligence primitive into economic outcomes Apps that benefit from different "minds" Company as a set of loops Shape of automation in the enterprise Consumer Headwinds to consumer AI Coding agents vs personal agents /loop make me happy Digital Mainstreet