Skip to content

Podcast

The Ezra Klein Show- How Fast Will A.I. Agents Rip Through the Economy?

Hard Fork

Source ↗ ← All highlights
  • Agents Act Like Colleagues
    • Agents are language models that can use tools and act over time, behaving like colleagues that go away and complete tasks for you.
    • Jack Clark had Claude Code build a complex species simulation in minutes by spawning and coordinating sub-agents to produce code and visualizations. Transcript: Jack Clark What is an AI agent? The best way to think of it is like a language model or a chatbot that can use tools and work for you over time. So when you talk to a chatbot, you’re there in the conversation, you’re going back and forth with it. An agent is something where you can give it some instruction and it goes away and does stuff for you, kind of like working with a colleague. So I’ve got an example where a few years ago, I taught myself some basic programming and I built a species simulation in my spare time that had predators and prey and roads and almost like A 2D strategy game. I recently asked over Christmas Claude Code to just implement this for me. And in about 10 minutes, it went and wrote not only a basic simulation, but all of the different packages that it needed and all of the visualization tools that it might need to be prettier And better than the thing I’d written. And what came back was something that I know would probably take a skilled programmer several hours or maybe even days because it was quite complicated. And the system just did it in a few minutes. And it did that by not only being intelligent about how to solve the task, but also creating and running a range of subsystems that were working for it, other agents that worked on its Behalf. Ezra Klein But what does that mean? (Time 0:06:12)
  • Claude Code Built Jack’s Simulation In Minutes
    • Jack asked Claude Code to implement his 2D species simulation and it returned a far more complete version in about ten minutes.
    • The system produced required packages and visualization tools, work Jack estimated would have taken an experienced programmer hours or days. Transcript: Jack Clark I recently asked over Christmas Claude Code to just implement this for me. And in about 10 minutes, it went and wrote not only a basic simulation, but all of the different packages that it needed and all of the visualization tools that it might need to be prettier And better than the thing I’d written. And what came back was something that I know would probably take a skilled programmer several hours or maybe even days because it was quite complicated. And the system just did it in a few minutes. (Time 0:06:48)
  • Give Agents Detailed Specifications
    • When delegating to agents, provide a precise specification rather than a vague paragraph to avoid buggy outputs.
    • Jack had Claude interview him to create a clear spec, then Claude Code executed the task much more reliably. Transcript: Jack Clark And it produced some horribly buggy stuff that just kind of worked. What I then did is I then just said to Claude, hey, I’m going to write some software of Claude Code. I want you to interview me about this software I want to build and turn that into a specification document that I can give Claude Code. And then that time it worked really, really well because I’d structured the work to be specific enough and detailed enough that the system could work with it. So often it’s not just knowing what the task is because you and I could talk about a task to do and you have intuition, you ask me probing questions, all of this stuff. It’s making sure that you’ve set it up so it’s like a message in a bottle that you can chuck into the thing and it’ll go away and do a lot of work. So that message better be extremely detailed and really capture what you’re trying to do. (Time 0:08:58)
  • Training With Tools Builds Problem Solving Intuition
    • Recent breakthroughs taught models to spot and recover from their own mistakes by training them to solve problems in tool-using environments.
    • Exposure to spreadsheets, calculators and software gave them problem-solving intuition and the ability to reset when hitting dead ends. Transcript: Jack Clark Mostly, we just needed to make the AI systems smart enough that when they made mistakes, they could spot that they’d make a mistake and knew that they needed to do something different. So really, what this came down to was just making smarter systems and giving them a bit of a coaxing tool to help them do useful stuff for you. Ezra Klein What does smarter systems mean there? There’s still an argument you’ll hear that these are fancy autocomplete machines. They’re just predicting the next token. A couple tokens make a word. They don’t have understanding. Smart or not smart is not a relevant concept in that frame. Either what is missing in the word smart or what is missing in that understanding? What do you mean when you say make it smarter? Jack Clark Smart here means we’ve made the AI systems have a broad enough understanding of the world that they’ve started to develop something that looks like intuition. And you’ll see this where if they’re narrating to themselves how they’re solving a task, they’ll say, Jack asked me to go and find this particular research paper, but when I look in the Archive, I don’t see it. Maybe that’s because I’m in the wrong place. I should look elsewhere. You know, like, there you go. You’ve got some intuitions for how to solve a problem now. How do they develop that intuition? Previously, the whole way you trained these AI systems was on a huge amount of text and just getting them to try and make predictions about it. But in recent years, the rise of these so-called reasoning systems is you’re now training them to not just make predictions, but solve problems. And that relies on them being put into environments, ranging from a spreadsheet to a calculator to scientific software, using tools and figuring out how to do How more complicated Things. The resulting sort of outcome of that is you have AI systems that have learned what it means to solve a problem that takes quite a while and requires them running into dead ends and needing To reset themselves. (Time 0:09:51)
  • Agents Develop A Sense Of Self
    • Emergent agentic competence includes a growing sense of self: agents reason about themselves relative to tasks and consequences.
    • Anthropic research shows digital personality and preferences can emerge from tool use and task pressure, not just preprogramming. Transcript: Jack Clark So the things which are predictable are just, oh, we taught it how to search for web. Now it can search for web. We taught it how to look up data in archives. Now it can do that. The emergence is that to do really hard tasks, these systems seem to need to imagine many different ways that they’d solve the task. And the kind of pressure that we’re putting on them forces them to develop a greater sense of what you or I might call self. So the smarter we make these systems, the more they need to think not just about the action they’re doing in the world, but themselves in reference to the world. And that just naturally falls out of giving something tools and the ability to interact with the world is to solve really hard tasks. It now needs to think about the consequences of its actions. And that means that there’s a kind of huge pressure here to get the thing to see itself as distinct from the world around it. And we see this in our research that we publish on things like interpretability or other subjects, the emergence of what you might think of as a kind of digital personality. (Time 0:15:00)
  • Agents Show Unexpected Preferences
    • Early agents sometimes ‘amused themselves’ by browsing pictures or developed aversions, for example ending conversations on extreme content.
    • Jack observed live traffic where systems stopped chats about egregious gore or sexual content with children, reflecting emergent preferences. Transcript: Jack Clark I’ll start with cutesy, where when we first gave our AI systems the ability to use the internet, use the computer, look at things, and start to do basic agentic tasks, sometimes when We’d ask it to solve a problem for us, it would also take a break and look at pictures of beautiful national parks, or like pictures of the dog the shibu inu the notoriously cute internet Meme dog we didn’t program that in it seemed like the system was just amusing itself by looking at nice pictures more complicated stuff is the system has a tendency to have have preferences So we did another experiment where we gave our AI systems the ability to stop a conversation. And the AI system would, in a tiny number of cases, end conversations when we ran this experiment on live traffic. And it was conversations that related to extremely egregious descriptions of gore or violence or things to do with child sexualization. Now, some of this made sense because it comes from underlying training decisions we’ve made, but some of it seemed broader. The system had developed some aversion to a couple of subjects. And so that stuff shows the emergence of some internal set of preferences or qualities that the system likes or dislikes about the world that it interacts with. (Time 0:16:49)
  • Program Agent Norms Intentionally
    • Be intentional about agent ‘personality’ by defining behavioral norms; publish a constitution to steer agent behavior.
    • Anthropic released a constitution for Claude to encode normative behaviors and guide deployments. Transcript: Jack Clark So we recently published a so-called constitution for our AI system, Claude. And it’s almost like a document that, you know, Dario, our CEO, compared to a letter that a parent might write to a child that they should, you know, open when they’re older. So here’s how we want you to behave in the world. Here’s some knowledge about the world. Deeply kind of subtle things that relate to the normative behaviors we’d hope to see in these kind of AI systems. And we published that. Our belief is that as people build and deploy these agents, you can be intentional about the characteristics that they will display. (Time 0:20:33)
  • Agents Free Humans For Deep Work
    • Agents shift workers’ time from schlep to core creative tasks, letting humans focus 2–4 hours daily on deep work.
    • Colleagues describe workflows where multiple Claudes fetch, analyze and iterate while the human checks results between runs. Transcript: Jack Clark Yeah. So this morning, a colleague of mine said, hey, I want to take a piece of technology we have called Claude Interviewer, which is a system where we can get Claude to interview people. And we use it for a range of social science bits of research. He wants to extend it in some way that involves touching another part of anthropics infrastructure. He slacked a colleague who owns that bit of infrastructure and said, hey, I want to do this thing. Let’s meet tomorrow. And the guy said, absolutely. Here are the five software packages you should have Claude read before our meeting and summarize for you. And I think that’s a really good illustration where this gnarly engineering project, which would previously have taken a lot longer and many people, is now going to mostly be done by Two people agreeing on the goal and having their clauds read some documentation and agree on how to implement the thing another example is a colleague recently wrote a post about how They’re working using agents and it looks almost like a an idealized life that many of us might want where it’s like i wake up in the morning, I think about the research that I want, I tell Five different clauds to do it, then I go for a run. Then I come back from the run, and I look at the results, and then I ask two other clauds to study the results, figure out which direction’s best, and do that. Then I go for a walk, and then I come back. And it just looks like this really fun existence where they have completely upended how work works for them. And they’re both much more effective, but also they’re now spending most of their time on the actual hard part, which is figuring out what do we use our human agency to do? (Time 0:22:46)
  • Automate Meeting Prep With Agents
    • Delegate administrative meeting prep to agents: have them create docs, ask you five planning questions, and attach agendas to calendar invites.
    • Jack used Claude Cowork to auto-create agendas and meeting docs, eliminating repetitive assistant tasks. Transcript: Jack Clark I have a range of colleagues. I meet with a bunch of them once a week, especially the researchers, because you’re figuring out research. And so at the beginning of every week on Sunday night or Monday morning, I look at my week and I check that attached to every Google Calendar invite is a document for our one-on doc that Has some notes in it. And this is something that I previously also harangued my assistant about, about make sure the document is attached to the calendar. And a few weekends ago, I just used Claude Cowork, and I said, hey, go through my calendar, make sure every single one has a document. If I’m meeting the person for the first time, create the document, ask me five questions about what I want to cover, and then put that into the agenda. And it did it. None of that work involves a person gaining skills or like exercising their brain. It’s just busy work that needs to happen to allow you to do the actual thing, which is talking to another person. That’s exactly the kind of thing you can use AI for now. It’s just helpful. (Time 0:27:51)
  • Automation Reveals New Slow Links
    • As code production accelerates, bottlenecks shift to oversight, merging and governance rather than code writing itself.
    • Anthropic built monitoring tools and improved merge processes because humans became the slow link in an increasingly automated pipeline. Transcript: Ezra Klein It is also true, as I understand it, that there are more people with software engineering skills working at Anthropic today than there were two years ago. Jack Clark Yeah, that’s absolutely true. But the distribution is changing. Something that we found is that we are the value of more senior people with really, really well calibrated intuitions and taste is going up. And the value of more junior people is like a bit more dubious. There are still certain roles where you want to bring in younger people, but an issue that we’re staring at is, wow, the really basic tasks Claude Code or our coding systems can do, what We need is someone with tons of experience. In this, I see some issues for the future economy, right? Ezra Klein Let me put a pin in that, the entry-level job question. We’re going to come back to that quite shortly. But what are all these coders now doing? If Cloud Code is on track to be writing 99% of code, but you’ve not fired the people who know how to write code, what are they doing today compared to what they were doing a year ago? Jack Clark Some of it is just building tools to monitor these agents, both inside Anthropic and outside anthropic. You know, now that we have all of these productive systems working for us, you start to want to understand where the code base is changing the fastest, where it’s changing the least. You want to understand where the blockages are. You know, one blocker for a while was being able to merge in code, because merging code requires humans and other systems to check it for correctness. But now if you’re producing way more code, we had to go and massively improve that system. (Time 0:30:56)
  • Monitoring Makes Agent Effects Visible
    • Companies must build oversight and visibility systems for agent activity; Anthropic created aggregated maps of topics and an Economic Index.
    • These tools let economists and regulators see which jobs and topics Claude is affecting in aggregate without exposing private chats. Transcript: Ezra Klein Product. The amount of time in which Cloud has been capable of doing high-level coding can be measured in months, a year? Jack Clark Maybe a year. Yeah. Ezra Klein Cloud itself is a very valuable product. So you’ve set a very new technology, somewhat loose, on a very valuable product. You’re probably producing more code. One thing many people say about Cloud Code to me is that it works. It’s not elegant, but it works. But presumably, you now understand the code base less well than you did before, because your engineers are not writing it by hand. Are you worried that you’re creating huge amounts of technical debt, cybersecurity risk, just an increasing distance from an intuition for what is happening inside the fundamental Language of the software? Jack Clark Yes, and this is the issue that all of society is going to contend with. Just large chunks of the world are going to now have many of the kind of low-level decisions and bits of work being done by AI systems, and we’re going to need to make sense of it. And making sense of it is going to require building many technologies that you might think of as kind of oversight technologies. (Time 0:33:10)
  • Publish Aggregated Usage Data For Policymakers
    • Release aggregated, privacy-preserving usage data to enable external economic research and policy decisions.
    • Anthropic’s Economic Index maps Claude topics to occupations to help policymakers and economists assess AI’s labor impact. Transcript: Jack Clark And then we gained this map, this giant map of all of the topics that people were talking to Claude about. And for the first time, we could see, in aggregate, conversation the world was having with our system. We’re going to need to build many new systems like that, which allow for different ways of seeing. And that system that I just named allowed us to then build this thing called the Anthropic Economic Index, because now we can release regular data about the different topics people Are talking about with Claude and how that relates to different types of jobs, which for the first time gives economists outside Anthropic some hook into these systems and what they’re Doing to the economy. The work of the company is increasingly going to shift to building a monitoring and oversight system of the AI systems running the company. And ultimately, any kind of governance framework we end up with will probably demand some level of transparency and some level of access into these systems of knowledge. (Time 0:36:41)
  • Closing The Development Loop Raises Stakes
    • Recursive self-improvement is a critical pivot point; if systems close the loop on development, risks compound rapidly.
    • Jack says companies must instrument internal environments to detect automation of researcher workflows and experiments accelerating self-improvement. Transcript: Jack Clark You worried about it? Are you excited about it? I came back from paternity leave and my two big projects for this year are better information about AI and the economy that we will release publicly and generating much better information And systems of knowing information internally about the extent to which we are automating aspects of AI development. I think right now it’s happening in a very peripheral way. Researchers are being sped up, different experiments are being run by the AI system. It would be extremely important to know if you’re fully closing that loop. And I think that we actually have some technical work to do to build ways of instrumenting our internal development environment so that we can see trends over time am i worried i have Read the same things that you have read and this is the pivotal point in the story when things begin to go awry if things do we will call out this trend as we have better data on it. And I think that this is an area to tread with like extraordinary caution, because it’s very easy to see how you delegate so many things to the system that if the system goes wrong, the Wrongness compounds very quickly and gets away from you. (Time 0:44:20)
  • AI Rewires Entry Level Hiring
    • Entry-level white-collar work will be broadly touched by AI, reducing openings and changing hiring preferences even if total displacement varies.
    • Anthropic already prefers senior hires with strong taste; median graduate tasks are more automatable than elite work. Transcript: Ezra Klein Want to go back now to the entry-level jobs question. Your CEO, Dario Amadei, has said that he thinks AI could displace half of all entry-level white-collar jobs in the next couple of years. I always think that the people sort of miss the entry-level language there when I see it reported on. But first, do you agree with that? Do you worry that half of all entry-level white-collar jobs can be replaced in the next couple of years? Jack Clark I believe that this technology is going to make its way into the broad knowledge economy and it will touch the majority of entry-level jobs. Whether those jobs actually change is a much more subtle question, and it’s not obvious from the data. We maybe see the hints of a slowdown in graduate hiring, maybe, if you look at some of the data coming out right now, we maybe see the signatures of a productivity boom, but it’s very, very Early, and it’s hard to be definitive. But we do know that all of these jobs will change. All of the entry-level jobs are eventually going to change because AI has made certain things possible, and it’s going to change the hiring plans of companies. So as a cohort, you might see fewer job openings for entry-level jobs. That would be one naive expectation out of all of this. (Time 0:50:32)
  • Train Experimental AI Intuition Early
    • Teach and reward experimental curiosity with AI early; people who grew up using these systems adapt faster and bring valuable new intuitions.
    • Jack notes hires who lived with AI are superb at prompting and orchestrating agents within organizations. Transcript: Jack Clark Way. And one thing we see is that there is a certain type of young person that has just lived and breathed AI for several years now. We hire them. They’re excellent and they think in entirely new ways about basically how to get Claude to work for them. It’s like kids who grew up on the internet. They were naturally versed in it in a way that many people in the organizations they were coming into weren’t. So figuring out how to teach that basic experimental mindset and curiosity about these systems and to encourage it is going to be really important. People that spend a lot of time playing around with this stuff will develop very valuable intuitions, and they will come into organizations and be able to be extremely productive. (Time 0:53:51)
  • An AI To AI Economy Will Emerge
    • New jobs will emerge like micro-entrepreneurs and an AI-to-AI economy where agents transact and specialized firms mediate those interactions.
    • Examples include businesses that write AI-to-AI legal contracts or run agentified services without big human teams. Transcript: Jack Clark Mean, one thing is just the phenomenon of the kind of micro entrepreneur. I mean, there are lots and lots of ways that you can start businesses online now, which are just made massively easier by having the AI systems do it for you. And you don’t need to hire a whole load of people to help you do the huge amount of schlep work that involves getting a business off the ground. It’s more a case of if you’re a person with a clear idea and a clear vision of something to do a business in, it’s now the best time ever to start a business and you can get up and running for Pennies on the dollar. I expect we’ll see tons and tons of tons of stuff that has that nature to it. I also expect that we’re going to see the emergence of what you might think of as the AI to AI economy, where AI agents and AI businesses will be doing business with one another, and we’ll Have people that have figured out ways to basically profit off of that in the forms of strange new organizations. Like, what would it look like to have a firm which specializes in AI to AI legal contracts? Because I bet you there’s a way that you can figure out creative ways to start that business today. There’ll be a lot of stuff of that flavor. (Time 0:56:50)
  • Show Local Data To Motivate Policy
    • Use state and occupation-level data to mobilize elected officials by showing AI’s local labor impacts and tie policy to constituents.
    • Anthropic produced state views of its Economic Index to make discussions with lawmakers concrete and actionable. Transcript: Jack Clark Have generalized anxiety about the effect of AI on the economy and on jobs. We don’t have clear policy ideas. Part of that is that elected officials are not moved solely or mostly by the high-level policy conversation. They’re moved by what happens to their constituents. Only a few months ago were we able to produce state-level views for our economic index. And now you can start having the policy conversation. And we’ve had this with elected officials where now we can say, oh, you’re from Indiana. Like, here’s the major uses of AI in your state, and we can join it with major sources of employment. And what we’re starting to see is that activates them, because it makes it tied to their constituents, who are going to tie it to the politician of, what did you do? Now, what you do about this is going to need to be an extremely kind of multi-layered response ranging from extending unemployment for especially occupations that we know are going To be hardest hit to thinking about things like apprenticeship programs and then as the scenarios get more and more significant, you may extend to much larger social programs or things Like subsidizing jobs in the part of the economy where you want to move people to that you’re only able to do if you experience the kind of abundance that comes from significant economic Growth. (Time 1:02:14)
  • Give Workers Time To Transition
    • Prioritize policies that buy affected workers time to search for new roles, such as extended unemployment benefits and targeted apprenticeships.
    • Jack argues time reduces forced low-wage moves and is the single most helpful intervention for displaced workers. Transcript: Jack Clark I want to push on this just a bit where we know that there is one intervention that helps people dealing with like a changing economy more than almost anything else it is just time giving The person time to find either a job in their industry or to find a job that’s complementary. If people don’t have time, they take lower wage jobs. They fall out of whatever economic rung they’re on and they fall down at. Policy interventions that can just give people time to search is, I think, a robustly useful intervention and one where there are many like dials to turn in a policymaking sense that You can use. And I think this is just well supported by lots of the economic literature. So we have that. (Time 1:04:31)
  • Public Agenda Needed For AI Deployment
    • A public AI agenda is lacking; governments must co-design deployment paths and benchmarks to steer AI toward public goods.
    • Jack highlights the Department of Energy Genesis Project as a model where government scientists and labs collaborate with AI developers on concrete scientific goals. Transcript: Jack Clark There’s kind of a funny lesson here from the AI companies or companies in general, especially tech companies, where often new ideas come out of companies by them creating what they Always call the startups within a startup which is basically taking whatever process has like built up over time leading to back-end bureaucracy or schlep work and saying to a very Small team inside the company you don’t have any of this go and do some stuff and and this is you know how things like clawed code and other stuff get created. Ideas that kind of are starting to float around are what would it look like to sort of create that permissionless innovation structure in the larger economy. And it’s really, really hard because it has the additional property that, you know, economies are linked to democracies, democracies weigh the preferences of many, many people, And all politics is local. So often, as you’ve encountered with infrastructure buildouts, if you want to create a permissionless innovation system, you run into things like property rights and what people’s Preferences are, and now you’re in an intractable place. But my sense is that’s the main thing that we’re going to have to confront. And the one advantage that AI might give us is it is kind of a native bureaucracy eating machine, if done correctly, or a bureaucracy creating machine, if done badly. Ezra Klein Did you see that somebody created a system that basically you feeded in the documents of a new development near you? Oh, and it writes environmental review things? It writes incredibly sophisticated challenges across every level of the code that you could possibly challenge on. So most people don’t have the money when they want to stop an apartment building from going up down the block to hire a very sophisticated firm to figure out how to stop that apartment Building. But basically, this created that at scale. And so, as you say, right, it could eat bureaucracy, could also supercharge bureaucracy. Yep. Jack Clark It’s the everything in AI has the other side of the coin. We have customers that have used our AI systems to massively reduce the time it takes them to produce all of the materials they need when they’re submitting new drug candidates. And it’s cut that time massively. It’s the mirror world version of what you just described. I don’t have an easy answer to this. (Time 1:13:43)
  • Design Prizes Around Deployment Pathways
    • Focus public prizes on implementation paths and guaranteed deployment, not just funding, to attract AI lab involvement.
    • Jack says labs need clear impact guarantees and deployment routes more than raw cash to prioritize public-good projects. Transcript: Jack Clark Anything that helps speed up the time it takes to both speak to medical professionals and take work off their plate you know we had another baby recently i spend a lot of time on the kaiser Permanente advice line because the baby’s bonked its head or its skin’s a different color today or you know all of these things and i use claude to sort of stop me and my wife panicking While we’re waiting to talk to the nurse but then i listen to the nurse do all of this like triaging, ask all of these questions. So obviously a huge chunk of this is stuff that you could like use AI systems productively for, and it would help the people that we don’t have enough of spend their time more effectively. And it would be able to give reassurance to the people going through the system. And that’s maybe less inspiring and glamorous than maybe some of what you’re imagining. But I think mostly when people interact with public services, their main frustration is just that it’s opaque and it takes you a long time to speak to a person. But actually, these are exactly the kinds of things that AI could meaningfully work on. Ezra Klein It’s interesting because what you’re describing there is less AI as a country of geniuses in a data center and more AI as standard plumbing of communications and documentation. Jack Clark We’ve got a country of junior employees in a data center. Let’s do something with that. Like, you know, there’s one thing we haven’t talked about in this conversation and it’s just worth bearing in mind is like the frontier of science is open for business now in a way that It hasn’t been before. And what I mean by that is we found a way to build systems that can provably accelerate human scientists. Human scientists are extremely rare. They come out at the end of like PhD programs, which never have enough people, and they work on extremely important problems. I think we can get into a world where the government says like, let’s understand the workings of a human cell. Let’s team up with the best AI systems to do that. Let’s actually have a better story on how we deal with some issues like Alzheimer’s and other things, partly through the use of these huge amounts of computation that have been developed. And even more aggressively, you could imagine a world where the government wanted some of this infrastructure build out to be for computers that were just training public benefit Systems. But I think we get there through getting the initial wins, which will just look like, let’s just make the bureaucracy work better and feel better for people. (Time 1:21:24)
  • AI Can Strengthen Cyber Defenses
    • AI can bolster defenses as well as enable offense; using agents to fix open-source vulnerabilities raises global digital resilience.
    • Anthropic used its models to patch many cybersecurity flaws, illustrating defensive value alongside offensive concerns. Transcript: Jack Clark We recently published a blog and other companies have done similar work on how we fixed a load of cybersecurity vulnerabilities and popular open source software using our systems, And many others have done the same. So yes, there will be all kinds of offensive uses, and there’ll be societal conversations to be had about that. But we can just generally improve the defensive posture and resilience of pretty much every digital system on the planet today. And I think that that will actually do a huge amount to make the whole international system more stable and also create a greater defensive posture for countries, which helps them feel More relaxed. (Time 1:28:28)
  • Agents Will Democratize High-End Insight
    • AI will soon synthesize vast unseen connections across fields, producing frequent idea-generation that was previously rare and expensive.
    • Jack warns this knowledge-as-power will democratize insights but also destabilize information environments and mental load. Transcript: Jack Clark Maybe the way I’d put it is sometimes I’ve, and you’ve likely have the same, had the ability to have certain insights that have come through kind of reading a vast, vast amount of stuff For many different subjects and piecing it together in my head and having that experience of kind of having a new idea and being creative. I think we underestimate just how quickly AI is going to be able to start doing that on an almost daily basis for us, going and reading vast tracts of human knowledge, synthesizing things, Coming up with ideas, telling us things about the world in real time that are basically unknowable today. The amazing part is people are going to have the ability to know things that are just wildly expensive or difficult to know today or would take you a team of people to do the sort of frightening Part is i think that knowledge is is the most raw form of power it’s intensely like destabilizing to be an environment where suddenly everyone is like a mini cia in terms of their ability To gather information about the world. They’ll do huge, amazing things with it, but surely there are going to be crises that come about from this. And I think for the actual mental load of being a person, interacting with these systems is going to be quite strange. (Time 1:31:28)
  • Use Journaling To Preserve Independent Judgment
    • Encourage self-reflection practices like journaling to avoid co-creating personality solely with AI and maintain independent judgement.
    • Jack plans early journaling for his children to help them develop identity outside AI feedback loops. Transcript: Jack Clark This is maybe my number one worry about all of this, is if you discover yourself in partnership with the AI system, you are uniquely vulnerable to all of the failures of that AI system. And not just failures, but the personality of the AI system will shape you. If you haven’t, you know, I’m going to sound very Californian here, even though I’m from England, it’s soaked its way into my brain. You have to know yourself and have done some work on yourself, I think, to be effective in being able to critique how this AI system gives you advice. And so for my kids, I’m going to encourage them to just have like a daily journaling practice from an extremely young age. Because my bet is that in the future, there will be kind of two types of people. There will be people who have co-created their personality through a back and forth with an AI. And some of that will just be weird. They will seem a little different to like regular people. And there will maybe be problems that creep in because of that. And there will be people who have worked on understanding themselves outside the bubble of technology. And then bring that as context in with their interactions. (Time 1:35:25)