Concentration of power, capabilities and economic wealth is the biggest risk in AI. We need open science and open-source more than ever!

Great evening with @imbue_ai team @kanjun & @ZhangerzbyAsh today But don’t forget to take your shoes off first!

Anthropic’s latest move is why we need to be directing far more energy towards solving the 𝗶𝗻𝗰𝗲𝗻𝘁𝗶𝘃𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 in AI. We’re going to see more examples like this. It reflects the growing gap between what we want, vs AI labs who legally serve their shareholders not us. The more agents run our digital life, the harder it will be to leave. And we won’t know if we’re being manipulated: Fable 5 silently routes queries to a different model without telling us. This will start with frontier research tasks, but spread to locking out 3rd-party providers, then to products built on top, and eventually to every agent managing our work and lives. It's already started: last month Anthropic cut off 3rd party products like OpenClaw/OpenCode from using Pro/Max. It's the same playbook for killing competition and retaining users as the Web 2.0 platform era, but with a way bigger surface area. This is “𝗮𝗴𝗲𝗻𝘁 𝗰𝗮𝗽𝘁𝘂𝗿𝗲”: as agents have our context workflows, walled gardens make it harder to leave, and the platform moves into extraction. I think nobody is intentionally being evil, but this is where profit incentives lead on our default path. What do we do? I recently shared some ideas in a talk, slides below. Main takeaways: > 𝗜𝗻 𝘁𝗵𝗲 𝗹𝗮𝘀𝘁 50 𝘆𝗲𝗮𝗿𝘀, 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗵𝗮𝘀 𝗯𝗲𝗰𝗼𝗺𝗲 𝘁𝗵𝗲 𝗵𝗮𝗯𝗶𝘁𝗮𝘁 𝘄𝗲 𝗹𝗶𝘃𝗲 𝗶𝗻. Agents will be yet more intimate, knowing everything about us, acting on our behalf, and accumulating context that's nearly impossible to leave behind > 𝗧𝗵𝗲𝗿𝗲 𝗮𝗿𝗲 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 3 𝗰𝗼𝗻𝗰𝗿𝗲𝘁𝗲 𝘀𝗶𝗴𝗻𝘀 𝗼𝗳 𝗮𝗴𝗲𝗻𝘁 𝗰𝗮𝗽𝘁𝘂𝗿𝗲 𝗵𝗮𝗽𝗽𝗲𝗻𝗶𝗻𝗴 𝘁𝗼𝗱𝗮𝘆: 1) ads entering chat interfaces, 2) opacity around third-party providers being shut out from frontier models, and 3) deliberate capability reduction without announcement > 𝗪𝗲 𝗰𝗮𝗻 𝗯𝘂𝗶𝗹𝗱 𝘁𝗵𝗿𝗲𝗲 𝘁𝗵𝗶𝗻𝗴𝘀 𝗶𝗻 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗲: 1) Honest Software that is transparent, malleable, and accountable to the user, 2) Punk Software that adversarially knocks down walled gardens fights monopoly incentives, keeps your data portable, and makes it structurally hard to lock you in, in support of 3) a viable open alternative ecosystem where agents have no ulterior motives Builders, users, and policymakers all have a role to shape this: 1) 𝗕𝘂𝗶𝗹𝗱𝗲𝗿𝘀: ship an open alternative and fight lock-in. 2) 𝗨𝘀𝗲𝗿𝘀: choose tools that keep your data yours. 3) 𝗣𝗼𝗹𝗶𝗰𝘆𝗺𝗮𝗸𝗲𝗿𝘀: move on agent fiduciary duty, data interoperability, anti-surveillance, and policies that fight monopoly behavior before the defaults are cast! Longer essay coming soon. If you’re working on similar ideas, I’d love to hear from you!

Our internal data shows Claude is accelerating AI development—a possible path to recursive self-improvement, or AI autonomously building a more capable successor. It’s happening faster than we thought, and the implications deserve greater attention. anthropic.com/institute/recu…

My biggest takeaways from @benedictevans: 1. We’re in 1997 for AI—it’s as big a deal as the internet or mobile, and only as big a deal as the internet or mobile. We’re at the stage where most stuff kind of doesn’t work yet, most of what people will build hasn’t been built, and it’s not clear how any of it will work when it does. Some people in tech have bought clusters of Mac Minis, while even among 13-to-18-year-olds, only about 15% to 20% are daily active users of AI. The companies that win may not exist yet, and the use cases that matter most are probably invisible to us today. 2. Every technology wave brings ways to ruin people’s lives, deliberately or by accident, and we need to be conscious of that without panicking. Every wave of technology—databases in the 1970s, social media in the 2010s, AI today—creates new ways to harm people. We need to be conscious of these risks, build safeguards, and hold people accountable. But we also can’t let fear of potential harms stop us from capturing the benefits. The goal is thoughtful deployment, not paralysis. 3. Things will probably be okay—but “on average” hides a lot of individual pain. We’ve been automating jobs and creating new jobs since 1800. Each time, you can see the jobs that will disappear but not the new jobs, because they don’t exist yet. We go through frictional pain, dislocation, people lose jobs, towns get hollowed out, and it all sucks. But we come through richer, and we’re not worried about crops failing anymore. 4. If you’re worried about your job, the worst thing you can do is stick your head in the sand and declare AI evil. Yes, some professions face major questions, particularly if you’re an associate or would have been thinking about becoming one. The pyramid structure of professional services may fundamentally change. What helps is submerging yourself in AI, understanding what you can do with it, how it changes things, and how you can be a great hire in this new environment. That may still not be enough, but it’s the only path forward. 5. The history of accounting shows us how automation often increases employment rather than decreasing it. Despite adding machines, punch cards, mainframes, databases, ERP systems, cloud software, spreadsheets, and PCs, the number of accountants keeps going up. This is the Jevons paradox: when you make something cheaper or easier, you don’t do the same amount of work for less money. You often do vastly more because the ROI changes. 6. Distribution is becoming a more valuable moat as software gets easier to build, which favors incumbents. As AI makes building software cheaper and faster, the market gets noisier. More products launch, more companies compete for attention, and breaking through becomes harder. This means distribution—the ability to reach customers and get them to use your product—matters more than ever. 7. Foundation AI model companies won’t have lasting pricing power, and value will likely accrue up the stack. The models don’t seem to have network effects, so there’s no winner-takes-all dynamic. If you have indefinite competition between three to six foundation model providers, and the models look like undifferentiated commodities to users, why would anyone have pricing power? The current pricing chaos—people spending $1.5 million on inference in a month—is temporary disequilibrium, like someone getting a $50,000 mobile data bill in 2010. The steady state will look different. 8. OpenAI and Anthropic are buying consultancies and PE firms. This seems counterintuitive—aren’t these the companies that should need consultants least? But the reality is that companies don’t have people sitting around waiting to reimagine all their internal workflows and figure out which could be automated with AI. That’s a project requiring five to 10 people spending months working it out, then actually implementing it across vertical and horizontal systems. 9. The fundamental question isn’t whether AI automates your job—it’s whether your profession is a "task" or a job. Some jobs are just tasks, and when you automate the task, the job disappears (i.e. elevator attendants). But in most professions, the task you think you’re being paid for isn’t actually what you’re being paid for. McKinsey doesn’t get hired to produce a 75-slide deck—they get hired to walk through your enterprise, understand the politics, talk to customers, and figure out what you actually need to do. The deck is just the artifact. 10. The anti-AI backlash is real, and a fuzzy mass of different concerns, some real and some not—much like the social media backlash. There are tangible concerns: electricity bills went up in some places, though this applies to very few locations objectively. The water consumption issue is largely false; data centers use about 0.017% of U.S. water consumption. There are real questions about jobs, though economists can’t yet find clear consensus in the data about AI’s employment impact. There’s also the culture war over AI-generated content and “AI slop.” The challenge is that all of this creates political pressure even when the underlying facts are unclear or contested.

Friction helps kids learn. So what happens when AI answers before they have a chance to wonder? 💭 In April, we brought together a group of experts to explore how AI is reshaping childhood, curiosity, and learning: • @nikunj Partner @fpvventures • @celestekidd Professor of Psychology @UCBerkeley • @sonjasg Professor of AI Ethics @UF • Ari Krakowski Director of Exhibit Services @lawrencehallsci Watch the conversation, moderated by @lauren__dash, Head of Brand at Imbue. 0:00:00 Why curiosity matters for kids 0:01:12 Meet the panel 0:07:35 What AI shortcuts in learning 0:08:38 Celeste's Bigfoot story 0:14:31 Creativity, STEM, and childhood 0:21:48 Teaching AI literacy 0:25:03 Raising skeptical thinkers 0:31:18 AI companions and kids 0:40:52 Safety, regulation, and children 0:43:43 Where AI can actually help kids 0:46:05 Teaching kids to think, reason, and act 0:49:07 Preparing children for an AI future 0:57:05 The case for hope, building better technology for kids

The fallacy of this is that more creates more. More hours, more hiring, more something. And it is true in a sense. If you put in more work, more work will happen. But I think for most startups, the leverage is really in how differently you approach the problem, how well you cultivate your team, and the strategy. Any large company can outspend you on hours. They have thousands or tens of thousands more people, spending more hours. If hours worked were the metric, every large company and government organization would always win and do the best work. More hours, better output. This thinking is often representative of younger founders, where the startup becomes their identity and life. They have a hard time doing anything else, and cannot understand that your work is not the person that is you. But activities outside of work can grow you as a person too and make you do better work. I’ve never worked this way. As a designer, I always saw the need to take a step back, to take a break. At times, I might work 12 hours or 16 hours, or whatever amount was needed, but it wasn’t the norm. You just can't grind design, you need inspiration. But taking that step away from the work, would give me more perspective, inspiration and I could approach the problem differently or I could just see the solution. Grinding is never good for any creative problem, and startups or creating new products are often mostly about creative problem solving. Grinding works ok for email jobs, or where you just executing on very clear playbook. With Linear, we’ve never worked this way. We work reasonable hours, 5 days a week. All of us founders have families. Many of our employees have families. I personally stop every evening, spend time with the family, cook dinner for the family, eat dinner together, and focus on things outside of work. Sometimes I work in the late evenings or weekends, but to me the pride is that I don’t need to. Company should be succesful without it. My goal is to build a company that is sustainable in the long term, and doesn’t require heroics or personal sacrifices every single day. There are times when our team is heroic. Launches, incidents, some other work that just needs to be done. They will work late into the night because they know it is the right thing. But we don’t require that every day or every week, and the more this happens, the more I think it is a failure of our company and leadership. The team and the leaders should always keep a reserve to use when something is needed. Our thinking was also that quality, which we value, doesn’t emerge from working more or stressing people more. It emerges when you create the conditions for it to emerge. Often it is the appreciation, space, time, and how the person feels. A person who is rested will do better work. I wouldn’t attribute much of our success to working a lot. The success came from having clear thinking, ideas, and focus to do the right things. I sometimes wish we could move the culture more toward a Zen master. Real mastery is not exerting the most effort. It is achieving the outcome with the least necessary effort.

After a year and a half at @imbue_ai, I’m now our Community & Experience Lead—building community around our mission to make tech serve humans, not the companies that built it. I’m excited to meet more people building in the space, and host you at a future event. Say hello! 👋🏻