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Agent Mode is here in Outlook! Copilot can now help run your inbox and calendar, triaging emails, rescheduling meetings, and helping you stay on top of what matters most.

Geoffrey Hinton says AGI is a silly term because intelligence is not one-dimensional AI will be jagged compared to humans: far better than us at some tasks, still weaker at others Superintelligence is a clearer idea It means AI becomes better than humans at almost every intellectual task we do, and it's coming

AI-native software engineering teams operate very differently than traditional teams. The obvious difference is that AI-native teams use coding agents to build products much faster, but this leads to many other changes in how we operate. For example, some great engineers now play broader roles than just writing code. They are partly product managers, designers, sometimes marketers. Further, small teams who work in the same office, where they can communicate face-to-face, can move incredibly quickly. Because we can now build fast, a greater fraction of time must be spent deciding what to build. To deal with this project-management bottleneck, some teams are pushing engineer:product manager (PM) some teams are pushing engineer:product manager (PM) ratios downward from, say, 8:1 to as low as 1:1. But we can do even better: If we have one PM who decides what to build and one engineer who builds it, the communication between them becomes a bottleneck. This is why the fastest-moving teams I see tend to have engineers who know how to do some product work (and, optionally, some PMs who know how to do some engineering work). When an engineer understands users and can make decisions on what to build and build it directly, they can execute incredibly quickly. I’ve seen engineers successfully expand their roles to including making product decisions, and PMs expand their roles to building software. The tech industry has more engineers than PMs, but both are promising paths. If you are an engineer, you’ll find it useful to learn some product management skills, and if you’re a PM, please learn to build! Looking beyond the product-management bottleneck, I also see bottlenecks in design, marketing, legal compliance, and much more. When we speed up coding 10x or 100x, everything else becomes slow in comparison. For example, some of my teams have built great features so quickly that the marketing organization was left scrambling to figure out how to communicate them to users — a marketing bottleneck. Or when a team can build software in a day that the legal department needs a week to review, that’s a legal compliance bottleneck. In this way, agentic coding isn’t just changing the workflow of software engineering, it’s also changing all the teams around it. When smaller, AI-enabled teams can get more done, generalists excel. Traditional companies need to pull together people from many specialties — engineering, product management, design, marketing, legal, etc. — to execute projects and create value. This has resulted in large teams of specialists who work together. But if a team of 2 persons is to get work done that require 5 different specialities, then some of those individuals must play roles outside a single speciality. In some small teams, individuals do have deep specializations. For example, one might be a great engineer and another a great PM. But they also understand the other key functions needed to move a project forward, and can jump into thinking through other kinds of problems as needed. Of course, proficiency with AI tools is a big help, since it helps us to think through problems that involve different roles. Even in a two-person team, to move fast, communication bottlenecks also must be minimized. This is why I value teams that work in the same location. Remote teams can perform well too, but the highest speed is achieved by having everyone in the room, able to communicate instantaneously to solve problems. This post focuses on AI-native teams with around 2-10 persons, but not everything can be done by a small team. I'll address the coordination of larger teams in the future. I realize these shifts to job roles are tough to navigate for many people. At the same time, I am encouraged that individuals and small teams who are willing to learn the relevant skills are now able to get far more done than was possible before. This is the golden age of learning and building! [Original text: deeplearning.ai/the-batch/issu… ]

Peter Diamandis giving the career advice nobody wants to hear but everyone needs. "The career of the future is not getting a job. I think the only career of the future is entrepreneurship." Most jobs will be automated. The ones that survive will look unrecognizable. The only hedge is building something yourself. FT @RaoulGMI @RealVision @PeterDiamandis @SalimIsmail

Marc Andreessen: "The fate of the world over the next fifteen hundred years is riding on the people who actually want to give it a shot." It is actually amazing that this has become a controversial thing, but we have this fundamental view that technology is an enormously powerful force in the world. The big problem with the world is simply that there is not enough technology, not enough information, and not enough intelligence. When you look at the world right now, it is just a very primitive and crude place compared to what it should be, and what it could be. Overall, especially in the Western world, everything is just stagnant. Without builders, there is nothing but stagnation. But every once in a while, you have somebody who comes along with that very special personality type of the entrepreneur. Someone who says, "I actually have an idea of how to make things fundamentally better, and I have a way to build a business, build a company, and build an empire around that." Those people are basically a rough movement against that stagnation. They are the ones who build the product, build a phenomenon, and actually make an impact on things. You always get this criticism from the corporate press or outside critics saying that VCs are funding the wrong things or entrepreneurs are building the wrong things. But nobody licensed us to do any of this. We didn't apply for a permit to get judged by somebody ahead of time. These are completely open fields. Anybody can build a product, start a company, or even try to be a VC. It is shocking how few people actually step up to the plate. We have these special sets of technologies that let us fundamentally improve things. The opportunity is there for anyone willing to take it.

You aren't going to get lucky repeating the same day. You need to go on more side quests. Weekly. Daily if you can. Even one tiny new decision can substantially increase your chances at luck.

Larry Ellison: “My standard advice to entrepreneurs is you can’t be successful as a small company doing the same thing everyone else is doing… If you’re an entrepreneur, you have to find errors in conventional wisdom.”

1985 was glorious. PC goes vertical. NES launches. Cable TV takes off. Back To The Future, Real Genius, Weird Science, DARYL. Top Gun filming. We Are The World, New Coke, Madonna, Wrestlemania. NSFNet. And constant looming threat of nuclear annihilation for adrenalinemaxxing.

Three things the leading AI models are quite good at: long term planning, idea generation, and taste. Sorry, but it's true.

Google Deepmind CEO gives his prediction on the final AGI architecture: Demis Hassabis just sat down on 20VC and broke down exactly how DeepMind is building toward true AGI, directly pushing back on the idea that LLMs are a dead end. He defends the current trajectory of the models, noting that “we’ve seen how successful these foundation models have been. They can do incredibly impressive things. I don’t think that’s going to go away. We’re still seeing returns from the scaling laws.” But he’s honest about the missing pieces for true superintelligence, saying “I think there might be… there’s a 50-50 chance there’s some things maybe missing that we still need to make breakthroughs in, perhaps their world models, these kinds of approaches.” And when he breaks down what the actual AGI system will look like, he frames it as one question is the LLM foundation model the key component only, or is it the total system. His answer - “I don’t think it’s going to get replaced. I think it’s going to get built on top of these foundation models, just like the way we do with our world models.”

One of the most important things that Elon taught me: People engaging in fraud are always the first and loudest to complain.

The biggest opportunity for would-be startup founders is AI. But the most underpriced opportunity is probably non-AI ideas. So if you have a good non-AI idea, go for it, because everyone else is going to overlook it.

1. I never said LLMs were not useful. They are, particularly with all the bells and whistles that are being added to them. I use them. 2. A robot-rich future can't be built with AIs that don't understand the physical world and don't anticipate the consequences of their actions. And LLMs really don't. 3. The future in the cartoon looks pretty dystopian TBH, but even a non-dystopian version will require world models and zero-shot planning abilities. 4. I rarely wear a suit and absolutely never wear a tie. 5. I would never ever place a coffee mug on top of a piece equipment. 6. I hope I'll look this young in 2032.

@deepseek_ai Used ChatGPT Images 2.0 to create this infographic summary of the technical paper.

🚀 DeepSeek-V4 Preview is officially live & open-sourced! Welcome to the era of cost-effective 1M context length. 🔹 DeepSeek-V4-Pro: 1.6T total / 49B active params. Performance rivaling the world's top closed-source models. 🔹 DeepSeek-V4-Flash: 284B total / 13B active params. Your fast, efficient, and economical choice. Try it now at chat.deepseek.com via Expert Mode / Instant Mode. API is updated & available today! 📄 Tech Report: huggingface.co/deepseek-ai/De… 🤗 Open Weights: huggingface.co/collections/de… 1/n

If you’re not making many mistakes, you must not be learning much. Mistakes and failures are ultimately more valuable to you than successes because they provide the best learning. I detailed some of my biggest mistakes—and what I learned from them—as part of the new MasterClass Executive program. At this stage in my life, I believe the most important thing I can do is to pass along everything I’ve learned to others, hoping that they find value in it and can avoid making the same mistakes I’ve made. If you’re interested in the program, you can apply for one of the limited spots here: mstr.cl/MasterClassExe…

Microsoft $MSFT for the first time will offer voluntary buyouts to a small percentage of its workforce The one-time retirement program will be available to U.S. workers at the senior director level and below whose years of employment and age add up to 70 or higher - Reuters

Peter Thiel says Steve Jobs wasn’t successful because he was an asshole. He was successful in spite of him being an asshole: “All the biographies you read on Jobs—the lesson you draw is that he succeeded because he yelled a lot and was this impossible manager.” “If that was true—how did he inspire his people?” “It’s something about the vision, the mission, the certainty that things were going to work that inspired people, in spite of whatever shortcomings he had.” Via @andrewrsorkin @dealbook

It's our home. This Earth Day, see our planet as our Artemis II astronauts saw it with these new images from the mission.

The greatest trait you can acquire is to work with tremendous intensity on things that matter to you, and more importantly, be strangely unbothered when those things don't work out.