Neel

@esskayesss_ Why not?

this cannot be real, right?

Brad Gerstner: Companies Will Pay 5x More for the Best AI, No Evidence of Pricing Pressure from Open Source @altcap: “Jason, you talked about summarizing a document, it may take 20,000 cheap tokens to do. Of course, shoot that to a lagging model or an open source model. But if you're talking about replacing a software engineer for two hours, that may take two million expensive tokens, and the consequence of using something that's 95% as good is really high. Because you have a long-running task, and if the task breaks early, or it breaks in the middle, or it breaks at the end, there's a huge cost to that.” @Jason: “You still burn the tokens, right? And back to this analogy I was using, you're pulling the slot machine, and you lose.” Brad: “And (you lose) the time and the compute. So if an AI agent is replacing a $200 an hour consultant, right? Take that as an example. So three consulting firms, they're competing. They need the smartest consultant. They're charging $200 an hour. The difference between spending $3 on a cheap model or $15 on an expensive model to replace a $200/hour consultant, it's just irrelevant. That inference cost difference is irrelevant if you're getting something that's bulletproof for $15, and so I think that's what we're seeing play out. The best evidence for all of this is just revenue growth. I'm talking about, what is Anthropic's revenue growth compared to OpenAI, compared to the open source models? Millions of independent actors are choosing every single day. The open source companies are growing, right? But they're growing selling something that is really, really cheap. And there's room in every single market for premium products, for mid-tier products, and for commodity products, and I think we see a lot of this token growth, people are speculating that the intelligence gap between that commodity stuff and the frontier stuff is going to collapse to the point that people won't pay for the frontier stuff. There is no evidence of that on the field today.”

NEW POST Birgitta Böckeler recently spent some time trying out running local LLMs for some programming tasks. In this memo she outlines the factors that influence how viable they are for the job. martinfowler.com/articles/explo…

Mind boggling to me that I can make a thing faster and there's always people that ask "but why?" What kind of mentality is that? The pursuit of excellence does not need justification. Also, I find in so many cases, we can't know the impact of an improvement until we do it. For example, one I've talked about before: Ghostty's high IO throughput has enabled terminal program (emulator and TUI) fuzzing at a speed thats incomparably fast to prior solutions. This has resulted in upstream patches to resolve issues in popular projects like btop, tmux, and more. Speed enabled that anecdotally example that lifted the tides of adjacent communities that don't rely on Ghostty technology at all. I didn't predict this. Make things better because they can be better and let the results naturally play out.

You know that "But, wait..." moment in every LLM thinking trace? I made it visible. I asked 8 models the same tricky probability question and rendered their reasoning as trees. Every time a model rejects its own idea and pivots, every "But...", every "Wait, actually...", a new branch grows. Same question. Completely different minds.

@ttorres I’m sorry you had to go through this.

@neeilk2028 These are all great in the ideal world. Too bad none of us live in the ideal worlds.

I booked a flight to Europe on Alaska Airlines. My first flight was on British Airways. They canceled the flight 8 hours before it was supposed to depart. I called and they rebooked me on a new flight that left at the same time and went through Frankfurt, but was in a mix of airlines: Lufthansa and Alaska. In Frankfurt, I missed my connection because passport control regularly takes 2-4 hours and I had 1 hour for my connection. When I went to the Lufthansa service desk, they said I needed to rebook through BA because they don’t fly to Bend. When I called BA they said it’s my fault I missed the flight. That I shouldn’t have accepted the rebooking because it was enough time to make the flight. Alaska says it’s BA’s responsibility. Lufthansa says it’s the airport’s responsibility. BA says it’s my responsibility. I was on a business class ticket. Three airlines that just lost a customer for life. So now I get no refund or compensation for that expensive ticket. And it’s up to me to pay for my own ticket home. This is why I don’t travel for work. Your 1-hour speaking engagement isn’t worth it. I have been awake for 30 hours and it will be another 20 before I get home. And the only ticket that gets me home in the next 24 hours is $8k.

@hopperlife @nikitita444 @SawyerMerritt @Scobleizer Or he fainted ??

@nikitita444 @SawyerMerritt @Scobleizer He was not steering

Tesla has just officially confirmed that the driver of the Model 3 that crashed into a home in Texas manually overrode the vehicle's self-driving system by pressing the accelerator pedal. The vehicle struck the home at 73 mph. A lot of mainstream media wrongly mislead people into thinking that it was the vehicles fault. Case closed.

@thatsmypjamz @TrungTPhan Folks try reduce Suffering by sharing .. or commensuration

My mother always said the big decline of neighborly companionship was due to AC. When she grew up without AC, everybody would have drinks and socialize and be outside trying to get fresh air whereas modern days everyone hides in their AC houses - AC did take some human socialization downward. An interesting perspective

Lee Kuan Yew: “Air conditioning was a most important invention for us, perhaps one of the signal inventions of history. It changed the nature of civilization by making development possible in the tropics. Without air conditioning you can work only in the cool early-morning hours or at dusk. The first thing I did upon becoming prime minister was to install air conditioners in buildings where the civil service worked. This was key to public efficiency."

“You escape competition through authenticity.” — Naval Ravikant

For people who don’t have a clear sense of the future they want, AI is just another mechanism of control But in the hands of someone with agency, AI is the breaker of chains, something that lets you do things no humans can do alone. AI can be a liberator if you choose agency.

Seneca on ANGER ‼️‼️

How long before coding agents stop saying "this will take ~2 weeks of work" for stuff I'm going to have them finish in less than 2 hours?

Claude is more designed to have a personality and hypnotize you by being the friend you never had as an autistic bullied child. Gpt 5.5 is a cyberweapon developed by an autistic bullied child that can help do anything that you want to do

Burnout isn’t caused by hard work. Instead, it’s a decoupling of effort and outcome. You push as hard as you can and nothing moves. Your company has become a supertanker with its own inertia.

My biggest takeaways from @danshipper: 1. The future of work will happen inside Codex or Claude Code. Instead of putting AI into your SaaS tool, you’ll use your SaaS tools inside your favorite AI agents' in-app browser. Dan spends all his time in Codex now—writing documents, managing email, doing research, everything. He's using Google Docs, PostHog, and everything he needs within the agent's in-app browser. The agent can see what he’s doing, and has all of his context, so he and his agent collaborate quickly and super effectively. 2. Automation is a lie—every automation needs a human. Dan's company doubled in size this year despite being incredibly AI-forward. Why? Because in order to make automation work well, you need humans making sure everything keeps working. This is why benchmarks are misleading—they measure AI on problems we’ve already framed and can score, but there’s always a higher frame. 3. PMs will win the AI era. Marcus, a former PM who previously ran Axios’s writing product, joined Every after getting super AI-pilled. Now he runs their product Spiral, and ships faster than anyone on the team. He pairs technical knowledge with spiky product sense, deep user empathy, and an eye for what matters. Dan thinks any PM who gets really AI-native will be incredibly dangerous because the building is done for you—what matters is figuring out what to build and if it’s great. 4. Full-stack designers are becoming superheroes. Designers used to make beautiful interactions that engineers didn’t want to build or couldn’t execute properly. Now designers don’t need to hand things off; they can build it themselves. Designers are naturally creative people, and AI is the perfect tool for them because it lets them bring their vision to life without the traditional bottlenecks. 5. SaaS is not dead. In fact, Dan is bullish on SaaS stocks. When users bring their own AI (via Codex or Claude Code) to use SaaS products, the user—not the SaaS company—pays for tokens. This saves SaaS company’s margins. Since the agents need their own seats, Dan predicts that agents will create massive new demand for SaaS because there will be tons of agents using these products at high volume. 6. Every company will have one “super-agent” inside their Slack that every employee will use. Dan initially thought every employee would have their personal work agent, like a shadow AI org chart, but he’s completely flipped his view. He realized agents need humans who care about them. When someone gets tired of maintaining their personal agent, it becomes useless. The winning model is one forward-deployed engineer or AI-savvy person who maintains a company-wide agent (like Shopify’s River or Viktor), and then it trickles down to more specialized team agents as models improve and become less fiddly. 7. The AI job apocalypse is not happening, but you do need to evolve to stay relevant. Models make yesterday’s human competence cheap. But because everyone uses the same models, it all looks the same if you use it the default way; it becomes commoditized slop. Humans then take that frozen competence and use it to make something new and interesting for their specific situation. The key: “ride the models”—use them for everything you do, try new models when they drop, keep turning over rocks. 8. We will read way more AI-generated writing, and we will like it. Human writing is incredibly important for things that matter, but for internal docs, planning, and email, AI-generated is often better because most people are bad at writing strategy documents. 9. Build software for humans and agents to use together. The current model is building a CLI that an agent uses independently. Instead, you and your agent should be using the app together. This creates new design challenges—agents can make a billion requests in three seconds, so you need approval flows, inboxes that summarize what happened, logs, and easy rollback. 10. Forward-deployed engineers are the new most essential role. The big model companies have teams of people managing their internal agents, and those teams aren’t going away. It’s different from traditional software building, and certain engineers love it. As models get better, this role will evolve—you’ll be managing more agents doing more things.

You might believe you should spend less time thinking about code because of AI. I strongly disagree! We’re watching this play out live where tons of AI generated code becomes a liability. At the end of the day, an engineer needs to be responsible / on call for code that gets shipped to production. If you don’t understand the system you’re trying to debug, you’re probably going to have a bad time. Yes, AI can help with all of this, if you set up the proper systems. You can have agents triage prod logs, look at errors, etc. You can speed up parts of the investigation, but an engineer needs to make the call. There might be serious customer or financial implications from that change. I expect the trend continue for trimming dependencies, vendoring code so you can modify it directly, preferring simpler systems with fewer abstractions, and spending waaaay more time thinking about system design and code maintenance. I’ve said this before, but it’s a great time to get familiar with CS fundamentals and some of the history behind what great software looks like. Many parts will be different in the coming years as AI progresses, but also a lot more than people realize will stay the same.

Bob McGrew has a framework I keep thinking about: in the AI future there are only two jobs. The Lone Genius and the Manager. That's it. Everything else gets absorbed. The Lone Genius is the person sitting alone at a computer, amplified 1000x by AI. One person with taste, vision, and relentless focus who can now do what used to take a team of 50. The Manager is the person who becomes CEO of their own "firm" where most of the employees are AI agents. They define the goals. They decide what matters. They coordinate. The AI does the execution. The Marxists will hear "two jobs" and panic. "What about everyone else?!" But here's what they're missing: AI doesn't shrink these two categories. It explodes them open. More people get to be geniuses. More people get to be managers. The barrier to entry for both just collapsed. What actually gets eliminated? David Graeber called them "bullshit jobs." Graeber was no libertarian! He inspired Occupy Wall Street. His words: "Huge swaths of people spend their entire working lives performing tasks they secretly believe don't really need to be performed. The moral and spiritual damage that comes from this situation is profound. It is a scar across our collective soul." Graeber said bullshit jobs are "a form of spiritual violence directed at the essence of what it means to be a human being." They induce "hopelessness, depression, and self-loathing." This is who the left should be fighting for. Not to preserve those jobs. To liberate people from them and give them better ones. The dirty secret of the modern economy: millions of people sit in roles so pointless that even they can't justify their existence. Compliance layers. Reporting layers. Coordination layers. Meeting-about-the-meeting layers. They know it's meaningless. It eats them alive. AI eats those layers. Good. That's a jailbreak. What I love about Bob's framework is where it points. The Lone Genius used to require a PhD, a lab, institutional backing. Now a 19-year-old with taste and Codex can ship what took a research team a year. The genius bottleneck was never talent. It was access. The Manager used to mean you needed to hire 50 people, raise money, build an org chart. Now you can orchestrate a fleet of AI agents from your laptop. The management bottleneck was never skill. It was capital. AI doesn't concentrate genius and management into fewer hands. It distributes them into more hands. The working class kid in West Virginia. The single mom in Ohio. The 55-year-old who got laid off and now builds software for the first time. Those are some of Bob's future geniuses and managers. The best founders I see at YC are already living this. They toggle between both modes in the same day. Morning: lone genius, creative insight, the thing nobody else sees. Afternoon: manager, spinning up agents, steering, shipping. The cycle time between genius and manager IS the new productivity metric. So when someone tells you AI means "only two jobs and everyone else starves," quote Graeber to them, they’ll get it. Graeber knew the real violence was making people do meaningless work and pretending it was dignity. AI ends that. More genius. More agency. Fewer spiritual prisons.