Interesting how even Anthropic's personal agent is still extremely enterprise-coded.

tell your agents ‘no gratuitous text/additions or eyebrow elements, read less is more by jakub’ you’re welcome

if you're using GrokBot or Muse to manage multiple projects, do you go with project specific 'vertical' agents or knowledge/skill specific 'horizontal' agents? and why?

The math doesn't math anymore. Ecommerce is a harder game than it's ever been and there's less money for brands than ever. Brands, Agencies, and Service Providers need to change their models.

This looks awesome

Any more questions?

We need to accelerate. We need a Protect Our Parks level effort here.

Hi, I'm Felix. I'm an eng lead working on Claude. I love watching people take flight and do things they never thought possible. We often describe that as "being more ambitious" when using AI, which is tricky advice to follow. I wrote a tutorial: felixrieseberg.com/how-to-be…

fuuuuuuuuuuuuuck

The Bar Tab A few weeks ago I wrote that the music was playing and everyone in venture capital felt obliged to dance. A few weeks after that I wrote that history doesn't repeat but it rhymes, and walked through the last time a band this loud played a room this full, which was 2000, A lot of you wrote in to ask what comes next. I didn't know. So I did what any responsible investor does when he doesn't know something: I got a napkin and started doing arithmetic I wasn't qualified to do. Fair warning. Every time I tried to make the numbers kinder, they got worse. This is a known property of napkins. Start with the size of the band. Microsoft, Alphabet, Amazon, Meta and Oracle spent about $230 billion on AI capex in 2024, about $400 billion in 2025, and have guided to around $800 billion this year. Goldman has $1.2 trillion next year and $1.4 trillion the year after. Add it up through 2029 and $4 trillion is the floor. Consensus is closer to $5.5 trillion. I'm using $4 trillion because I'm an optimist and because I only had one napkin. Now, a bar tab is a thing that eventually gets handed to somebody. What does $4 trillion have to earn? Say 12%, which is about what a sane person wants for setting that much money on fire in West Texas. That's $480 billion a year. Call it $500 billion of free cash flow by 2029. Then I remembered a GPU is not a building. Amazon depreciates servers over five years, and Michael Burry thinks that's charitable. If you want 12% on $4 trillion of equipment that'll be scrap before my youngest finishes college, you need about $1.1 trillion a year. I'm going to keep using $500 billion anyway. When the story is bad enough, you don't need to make it worse to get people's attention. Five hundred billion of free cash flow at a 50% gross margin means $1 trillion of revenue. Somebody has to write checks to the hyperscalers for a trillion dollars a year of AI compute by 2029. Who? Some of it is the hyperscalers buying themselves a round: Meta's capex buys Meta's ads. But the biggest outside checks come from the frontier labs, the handful of companies that train the models the rest of us build on. Full disclosure: Menlo is an investor in this category, so adjust for the fact that I'm a man with a drink in his hand telling you the bar is overpriced. Say the labs are half of it. That's $500 billion of revenue, as a group, by 2029. Their combined run-rate today is north of $100 billion. Three years ago it was $2 billion. That is the fastest revenue ramp in the history of business, it isn't close, and it still isn't enough. To hit $500 billion the category has to grow fivefold in three years, 70% a year, every year, from a base the size of Coca-Cola. There's no precedent for that. There was also no precedent for the last three years, which is the whole trouble with this moment. The people who've been right so far have been right by ignoring precedent, and a man who got rich ignoring precedent is the last man alive who'll start consulting it. So there are two forks in the road, and both of them are toll roads. Fork one: the labs don't get there, and the $4 trillion doesn't earn its keep. People say "debt crisis." Careful. Here's where the rhyme matters. In 2001 the trillion dollars of telecom debt was junk paper issued by CLECs with no revenue, and the bondholders got sixteen cents. Today the four big spenders fund about 70% of their capex out of operating cash flow and carry AA ratings. The debt lives at Oracle, CoreWeave, xAI, Meta's off-balance-sheet vehicles and in about $350 billion of private credit. That's not 2008. It's 2001 with better credit: write-downs, capex cuts, and a few bodies in the second tier. The band doesn't die. It just stops taking requests. And here's the thing nobody says out loud. About 40% of AI capex is GPUs, and Nvidia's gross margin is 75%. So roughly 30% of the $4 trillion has already been earned. By Nvidia. The return on this capex has, in a sense, already happened. It went to one company in Santa Clara, which is like finding out the band got paid up front and the tab is yours. Fork two: the labs do get there. Then $500 billion a year has to come from somewhere, because money is not conjured, whatever the deck says, and there are only three somewheres. It comes out of wages. US compensation of employees is $15.7 trillion. Five hundred billion is 3.2% of it. At $100,000 per fully loaded worker, that's five million jobs, which takes unemployment from 4.3% to about 7%. "Productivity" is what we call this at conferences. Or it comes from growth. Five hundred billion is 1.6% of US GDP. If AI makes the economy 1.6% bigger than it would otherwise have been, nobody loses, every deck was right, and I owe a great many people an apology, which I'll deliver cheerfully and from a boat. I'd note that AI is already adding about a point and a half to GDP growth, but that's the capex itself, not the output. So far the bar tab is being counted as revenue, which bartenders have been doing since the invention of bartenders. Or it comes out of someone else's budget. The world spends $1.44 trillion a year on software. Five hundred billion is 35% of every software dollar on earth, so software can't be the whole answer, but it's the first place the labs will look, and the IT services and outsourcing budgets behind it are another $1.7 trillion. In this version the thing that goes to zero isn't the hyperscalers. It's SaaS at ten times revenue. If you're long SaaS at ten times revenue, read that sentence twice. The honest answer is a blend, and the whole game is which blend, because every blend has a different loser. In fork one it's the lenders. In the wage version it's people with jobs. In the budget version it's everyone long software. In the growth version nobody loses, which is exactly why I don't trust it. In thirty years I have never seen a version where nobody loses. I have seen a great many decks that promised one. Reflexivity ends when the story and the cash get too far apart. The story is $4 trillion. The cash is $100 billion, growing faster than anything ever has. That gap closes one of two ways: the cash catches up to the story, or the story comes down to the cash. The music's still playing, the floor's still full, and I'm out there with everybody else, because that's the job. But I've started keeping one eye on the bar. In 2000 the bartender came around in about ninety days. He didn't ask who'd had the most fun. He just asked whose card was open.

Riding my Lyft scooter down Chestnut when a Blue Angel flies over

Wonder how much the Blue Angels are spending on tokens

every app is being CHAT-ified.

I am surprised that people are surprised this is how I prompt Claude. Talk to Claude the way you would a coworker. There's no secret to prompting. There's no need to be overly scaffolded or prescriptive for most tasks -- give Claude a goal, and it will figure it out. Back in the Sonnet 3.5 days, your prompt mattered a lot. Nowadays, it's much more important to communicate to the model: 1. What you want it to do 2. How much effort you want it to spend 3. How it should verify that it did the right thing

🚨 The @a16z Consumer AI Top 100 is back - but this time, it's 150! We added revenue data from @yipitdata to track how consumers are spending not just time, but money, on AI. The result? A story of heavy concentration among AI's power users. Our takeaways 👇

there’s something quite awkward about all the personal agents in the current hype cycle muse, grok bot, instinct, dots, and whatever google, anthropic will come up with none of them is “mine” i’d be trusting a vendor for some of the most sensitive data about me and bet on them handling it with extreme responsibility. what if they have a data leakage incident? what if they hired a wrong employee? what if they simply break bad? i’d also be betting on their model. right now i’ve set up a lot of my stuff in grok bot, but what if their model falls behind? what if another model becomes 10x better on pure intelligence? then i’ll either miss out on a better assistant, or have to bite the bullet and do a full migration i’d also have to bet on their product. they may not build the features i want. they may not allow me to customize enough. and one day my assistant may show me ads that feels like way too much betting than what i’d be comfortable with, if i truly want literally everything in my life to go through and be taken care of by an assistant with all that considered, i’m more bullish on open source personal agents that run on people’s own computers. but i think these open source projects have to break out of the assumption that their users are developers and they can just throw a repo at them and ask them to launch a terminal a well polished, fool proof, community maintained, vendor agnostic, free personal agent that everyone can run by themselves without locking into a SaaS - that’s what i think many people will need

Who’s got an office chair rec? Need to step my game up