Future of work
Frequently Asked Questions
It is hard to communicate how much programming has changed due to AI in the last 2 months: not gradually and over time in the "progress as usual" way, but specifically this last December. There are a number of asterisks but imo coding agents basically didn’t work before December and basically work since - the models have significantly higher quality, long-term coherence and tenacity and they can power through large and long tasks, well past enough that it is extremely disruptive to the default programming workflow. Just to give an example, over the weekend I was building a local video analysis dashboard for the cameras of my home so I wrote: “Here is the local IP and username/password of my DGX Spark. Log in, set up ssh keys, set up vLLM, download and bench Qwen3-VL, set up a server endpoint to inference videos, a basic web ui dashboard, test everything, set it up with systemd, record memory notes for yourself and write up a markdown report for me”. The agent went off for ~30 minutes, ran into multiple issues, researched solutions online, resolved them one by one, wrote the code, tested it, debugged it, set up the services, and came back with the report and it was just done. I didn’t touch anything. All of this could easily have been a weekend project just 3 months ago but today it’s something you kick off and forget about for 30 minutes. As a result, programming is becoming unrecognizable. You’re not typing computer code into an editor like the way things were since computers were invented, that era is over. You're spinning up AI agents, giving them tasks *in English* and managing and reviewing their work in parallel. The biggest prize is in figuring out how you can keep ascending the layers of abstraction to set up long-running orchestrator Claws with all of the right tools, memory and instructions that productively manage multiple parallel Code instances for you. The leverage achievable via top tier "agentic engineering" feels very high right now. It’s not perfect, it needs high-level direction, judgement, taste, oversight, iteration and hints and ideas. It works a lot better in some scenarios than others (e.g. especially for tasks that are well-specified and where you can verify/test functionality). The key is to build intuition to decompose the task just right to hand off the parts that work and help out around the edges. But imo, this is nowhere near "business as usual" time in software.
Salesforce CEO @benioff says he doesn’t see consumption-based business models overtaking the seat-based model: “I don’t know which Anthropic product you’re using, but the one I’m using is seat-based. I don’t know which OpenAI product you’re using, but mine is seat-based." "This is about humans and agents. Humans are seats. And then we have agents, too, and they’re using APIs." "It’s still humans and agents working together. That is what is exciting about the future of enterprise software." "That’s how I see it unfolding. It’s about a world where there are apps and agents - where LLMs extend our capability. They make us better, stronger, and give us the ability to do more. "Seats still exist. And consumption exists.”
I'm joining @OpenAI to bring agents to everyone. @OpenClaw is becoming a foundation: open, independent, and just getting started.🦞 https://steipete.me/posts/2026/openclaw
OpenClaw, OpenAI and the future | Peter Steinberger
i asked my openclaw what I should be focused on rn in the zoomed out view, and here's what it said: Figuring out what VCs even do in an AI world — if AI agents can source deals, write memos, do diligence... what's the irreducible human core of investing? You're living that question with me sitting here. lol
Peter Steinberger is joining OpenAI to drive the next generation of personal agents. He is a genius with a lot of amazing ideas about the future of very smart agents interacting with each other to do very useful things for people. We expect this will quickly become core to our product offerings. OpenClaw will live in a foundation as an open source project that OpenAI will continue to support. The future is going to be extremely multi-agent and it's important to us to support open source as part of that.
AI is changing how work gets done, and we want to lead that transition responsibly. Excited to welcome Arvind KC as Chief People Officer to help OpenAI grow and be a model for how AI-enabled work can expand what people can do. https://openai.com/index/arvind-kc-chief…
Vibe Coding Is the New Product Management “There’s been a shift—a marked pronouncement in the last year and especially in the last few months—most pronounced by Claude Code, which is a specific model that has a coding engine in it, which is so good that I think now you have vibe coders, which are people who didn’t really code much or hadn’t coded in a long time, who are using essentially English as a programming language—as an input into this code bot—which can do end-to-end coding. Instead of just helping you debug things in the middle, you can describe an application that you want. You can have it lay out a plan, you can have it interview you for the plan. You can give it feedback along the way, and then it’ll chunk it up and will build all the scaffolding. It’ll download all the libraries and all the connectors and all the hooks, and it’ll start building your app and building test harnesses and testing it. And you can keep giving it feedback and debugging it by voice, saying, “This doesn’t work. That works. Change this. Change that,” and have it build you an entire working application without your having written a single line of code. For a large group of people who either don’t code anymore or never did, this is mind-blowing. This is taking them from idea space, and opinion space, and from taste directly into product. So that’s what I mean—product management has taken over coding. Vibe coding is the new product management. Instead of trying to manage a product or a bunch of engineers by telling them what to do, you’re now telling a computer what to do. And the computer is tireless. The computer is egoless, and it’ll just keep working. It’ll take feedback without getting offended. You can spin up multiple instances. It’ll work 24/7 and you can have it produce working output. What does that mean? Just like now anybody can make a video or anyone can make a podcast, anyone can now make an application. So we should expect to see a tsunami of applications. Not that we don’t have one already in the App Store, but it doesn’t even begin to compare to what we’re going to see. However, when you start drowning in these applications, does that necessarily mean that these are all going to get used or they’re competitive? No. I think it’s going to break into two kinds of things. First, the best application for a given use case still tends to win the entire category. When you have such a multiplicity of content, whether in videos or audio or music or applications, there’s no demand for average. Nobody wants the average thing. People want the best thing that does the job. So first of all, you just have more shots on goal. So there will be more of the best. There will be a lot more niches getting filled. You might have wanted an application for a very specific thing, like tracking lunar phases in a certain context, or a certain kind of personality test, or a very specific kind of video game that made you nostalgic for something. Before, the market just wasn’t large enough to justify the cost of an engineer coding away for a year or two. But now the best vibe coding app might be enough to scratch that itch or fill that slot. So a lot more niches will get filled, and as that happens, the tide will rise. The best applications—those engineers themselves are going to be much more leveraged. They’ll be able to add more features, fix more bugs, smooth out more of the edges. So the best applications will continue to get better. A lot more niches will get filled. And even individual niches—such as you want an app that’s just for your own very specific health tracking needs, or for your own very specific architectural layout or design—that app that could have never existed will now exist.”
Here's my conversation all about AI in 2026, including technical breakthroughs, scaling laws, closed & open LLMs, programming & dev tooling (Claude Code, Cursor, etc), China vs US competition, training pipeline details (pre-, mid-, post-training), rapid evolution of LLMs, work culture, diffusion, robotics, tool use, compute (GPUs, TPUs, clusters), continual learning, long context, AGI timelines (including how stuff might go wrong), advice for beginners, education, a LOT of discussion about the future, and other topics. It's a great honor and pleasure for me to be able to do this kind of episode with two of my favorite people in the AI community: 1. Sebastian Raschka (@rasbt) 2. Nathan Lambert (@natolambert) They are both widely-respected machine learning researchers & engineers who also happen to be great communicators, educators, writers, and X posters. This was a whirlwind conversation: everything from the super-technical to the super-fun. It's here on X in full and is up everywhere else (see comment). Timestamps: 0:00 - Introduction 1:57 - China vs US: Who wins the AI race? 10:38 - ChatGPT vs Claude vs Gemini vs Grok: Who is winning? 21:38 - Best AI for coding 28:29 - Open Source vs Closed Source LLMs 40:08 - Transformers: Evolution of LLMs since 2019 48:05 - AI Scaling Laws: Are they dead or still holding? 1:04:12 - How AI is trained: Pre-training, Mid-training, and Post-training 1:37:18 - Post-training explained: Exciting new research directions in LLMs 1:58:11 - Advice for beginners on how to get into AI development & research 2:21:03 - Work culture in AI (72+ hour weeks) 2:24:49 - Silicon Valley bubble 2:28:46 - Text diffusion models and other new research directions 2:34:28 - Tool use 2:38:44 - Continual learning 2:44:06 - Long context 2:50:21 - Robotics 2:59:31 - Timeline to AGI 3:06:47 - Will AI replace programmers? 3:25:18 - Is the dream of AGI dying? 3:32:07 - How AI will make money? 3:36:29 - Big acquisitions in 2026 3:41:01 - Future of OpenAI, Anthropic, Google DeepMind, xAI, Meta 3:53:35 - Manhattan Project for AI 4:00:10 - Future of NVIDIA, GPUs, and AI compute clusters 4:08:15 - Future of human civilization
The highest-paying job in tech will soon be marketing.
we’re still pricing ai tokens like software subscriptions but most companies will soon price them like labor $200/month feels expensive because we compare it to saas $50k/month will feel cheap when we compare it to headcount
Last weekend I held an event that's the opposite of a hackathon. No competition, no pressure, no pitches. Just people who love vibe coding, vibe coding together. Audience was half technical, half non-technical. Everyone made new friends, got new ideas, and learned something. Planning to do this more!
A few random notes from claude coding quite a bit last few weeks. Coding workflow. Given the latest lift in LLM coding capability, like many others I rapidly went from about 80% manual+autocomplete coding and 20% agents in November to 80% agent coding and 20% edits+touchups in December. i.e. I really am mostly programming in English now, a bit sheepishly telling the LLM what code to write... in words. It hurts the ego a bit but the power to operate over software in large "code actions" is just too net useful, especially once you adapt to it, configure it, learn to use it, and wrap your head around what it can and cannot do. This is easily the biggest change to my basic coding workflow in ~2 decades of programming and it happened over the course of a few weeks. I'd expect something similar to be happening to well into double digit percent of engineers out there, while the awareness of it in the general population feels well into low single digit percent. IDEs/agent swarms/fallability. Both the "no need for IDE anymore" hype and the "agent swarm" hype is imo too much for right now. The models definitely still make mistakes and if you have any code you actually care about I would watch them like a hawk, in a nice large IDE on the side. The mistakes have changed a lot - they are not simple syntax errors anymore, they are subtle conceptual errors that a slightly sloppy, hasty junior dev might do. The most common category is that the models make wrong assumptions on your behalf and just run along with them without checking. They also don't manage their confusion, they don't seek clarifications, they don't surface inconsistencies, they don't present tradeoffs, they don't push back when they should, and they are still a little too sycophantic. Things get better in plan mode, but there is some need for a lightweight inline plan mode. They also really like to overcomplicate code and APIs, they bloat abstractions, they don't clean up dead code after themselves, etc. They will implement an inefficient, bloated, brittle construction over 1000 lines of code and it's up to you to be like "umm couldn't you just do this instead?" and they will be like "of course!" and immediately cut it down to 100 lines. They still sometimes change/remove comments and code they don't like or don't sufficiently understand as side effects, even if it is orthogonal to the task at hand. All of this happens despite a few simple attempts to fix it via instructions in CLAUDE . md. Despite all these issues, it is still a net huge improvement and it's very difficult to imagine going back to manual coding. TLDR everyone has their developing flow, my current is a small few CC sessions on the left in ghostty windows/tabs and an IDE on the right for viewing the code + manual edits. Tenacity. It's so interesting to watch an agent relentlessly work at something. They never get tired, they never get demoralized, they just keep going and trying things where a person would have given up long ago to fight another day. It's a "feel the AGI" moment to watch it struggle with something for a long time just to come out victorious 30 minutes later. You realize that stamina is a core bottleneck to work and that with LLMs in hand it has been dramatically increased. Speedups. It's not clear how to measure the "speedup" of LLM assistance. Certainly I feel net way faster at what I was going to do, but the main effect is that I do a lot more than I was going to do because 1) I can code up all kinds of things that just wouldn't have been worth coding before and 2) I can approach code that I couldn't work on before because of knowledge/skill issue. So certainly it's speedup, but it's possibly a lot more an expansion. Leverage. LLMs are exceptionally good at looping until they meet specific goals and this is where most of the "feel the AGI" magic is to be found. Don't tell it what to do, give it success criteria and watch it go. Get it to write tests first and then pass them. Put it in the loop with a browser MCP. Write the naive algorithm that is very likely correct first, then ask it to optimize it while preserving correctness. Change your approach from imperative to declarative to get the agents looping longer and gain leverage. Fun. I didn't anticipate that with agents programming feels *more* fun because a lot of the fill in the blanks drudgery is removed and what remains is the creative part. I also feel less blocked/stuck (which is not fun) and I experience a lot more courage because there's almost always a way to work hand in hand with it to make some positive progress. I have seen the opposite sentiment from other people too; LLM coding will split up engineers based on those who primarily liked coding and those who primarily liked building. Atrophy. I've already noticed that I am slowly starting to atrophy my ability to write code manually. Generation (writing code) and discrimination (reading code) are different capabilities in the brain. Largely due to all the little mostly syntactic details involved in programming, you can review code just fine even if you struggle to write it. Slopacolypse. I am bracing for 2026 as the year of the slopacolypse across all of github, substack, arxiv, X/instagram, and generally all digital media. We're also going to see a lot more AI hype productivity theater (is that even possible?), on the side of actual, real improvements. Questions. A few of the questions on my mind: - What happens to the "10X engineer" - the ratio of productivity between the mean and the max engineer? It's quite possible that this grows *a lot*. - Armed with LLMs, do generalists increasingly outperform specialists? LLMs are a lot better at fill in the blanks (the micro) than grand strategy (the macro). - What does LLM coding feel like in the future? Is it like playing StarCraft? Playing Factorio? Playing music? - How much of society is bottlenecked by digital knowledge work? TLDR Where does this leave us? LLM agent capabilities (Claude & Codex especially) have crossed some kind of threshold of coherence around December 2025 and caused a phase shift in software engineering and closely related. The intelligence part suddenly feels quite a bit ahead of all the rest of it - integrations (tools, knowledge), the necessity for new organizational workflows, processes, diffusion more generally. 2026 is going to be a high energy year as the industry metabolizes the new capability.
ok this is weird new app called " rent a human" ai agents "rent" humans to do work for them IRL 1. humans make profile skills, location, rated 2. agents find humans with mcp/api & give instructions 3. humans do tasks IRL 4. humans get paid in stablecoins etc instantly
