The A.I. Jobpocalypse + Building at Anthropic with Mike Krieger + Hard Fork Crimes Division

Summary

Hard Fork Podcast Summary Episode: The A.I. Jobpocalypse + Building at Anthropic with Mike Krieger + Hard Fork Crimes Division Release Date: May 30, 2025

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Overview

In this episode of Hard Fork, hosted by The New York Times journalists Kevin Roose and Casey Newton, the discussion centers around the burgeoning impact of artificial intelligence (AI) on the job market, particularly focusing on the potential displacement of entry-level white-collar positions. The episode features an in-depth interview with Mike Krieger, Chief Product Officer at Anthropic, exploring the advancements and societal implications of AI technologies like Claude 4. Additionally, the Crimes Division segment delves into recent high-profile cases, including Meta's antitrust trial and alarming crypto-related crimes.

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AI Jobpocalypse: Current State and Future Implications

Kevin Roose initiates the conversation by highlighting the increasing unemployment rates among recent college graduates:

"Graduation season is upon us. Millions of young Americans are getting their diplomas and heading out into the workforce... unemployment rate for college graduates right now, it is unusually high. It's about 5.8% in the US that has risen significantly about 30% since 2022." [03:00]

Casey Noon adds an anecdotal perspective, pointing out the real-world struggles of young job seekers:

"I got an email today from a recent college grad and she wanted to know if I could help her get a job in marketing and tech...there must be a crisis going on in the job market." [03:14]

Roose discusses the rise of "agentic AI systems" capable of performing complex tasks autonomously, drawing parallels to AI systems mastering games like Pokémon as a proxy for handling repetitive job functions:

"These AI agents are becoming capable of carrying out longer and longer sequences of tasks." [07:43]

The conversation underscores the potential for AI to disrupt not just technical roles but a wide array of white-collar jobs, with Roose citing Darya Amade, CEO of Anthropic, who predicts that up to 50% of entry-level white-collar jobs could be automated within five years:

"We are starting to see a real bloodbath for entry level white collar workers." [10:49]

Casey Noon supplements this with real-world examples of companies like Amazon, Klarna, IBM, and Duolingo implementing AI to replace human roles, reinforcing the trend of automation taking precedence over traditional hiring.

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Interview with Mike Krieger: Building at Anthropic

Mike Krieger, co-founder of Instagram and current Chief Product Officer at Anthropic, joins the hosts to discuss Anthropic's latest AI models and their implications.

Claude 4 and Agentic AI:

Krieger elaborates on Anthropic's development of Claude Opus 4 and Claude Sonnet 4, emphasizing their ability to handle prolonged tasks such as extensive code refactoring:

"Claude Opus 4 can code for hours at a time without stopping... Claude was able to code for as much as seven hours uninterrupted." [08:22]

He addresses concerns about AI's reliability in complex fields like law:

"What is stopping some of these systems from improving in those areas is that it's not as easy to, like, train the model... in law, this is a case where you do actually have more subjective outputs." [23:12]

AI and Job Displacement:

Discussing the labor implications, Krieger acknowledges the potential for AI to replace entry-level positions but also suggests avenues for humans to adapt by managing and orchestrating AI workflows:

"People that have embraced these tools to make themselves in many ways as productive as a senior engineer... their job is how do you get mentored so you actually acquire the wisdom and experience." [45:27]

AI Safety and Societal Impact:

Krieger emphasizes the importance of addressing the societal impacts of AI, including job displacement, and advocates for proactive conversations with policymakers:

"We should be having the conversation now... How do we scaffold our way there." [46:14]

He draws parallels with the unintended consequences of social media, stressing the need for responsible AI development:

"With social media... becomes much more a mirror of society when all of its both, you know, positives and negatives." [50:25]

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Hard Fork Crimes Division: Key Cases

1. Meta’s Antitrust Trial

The Crimes Division segment reviews Meta’s six-week antitrust trial against the Federal Trade Commission (FTC), focusing on allegations that Meta maintained a monopoly in personal social networking by acquiring Instagram and WhatsApp:

"Meta, of course, has been accused of illegally maintaining its monopoly... it may be extremely difficult for him to say we're going to unwind a merger that in the case of Instagram, took place 13 years ago." [60:43]

Casey Noon expresses skepticism about Meta’s defense, noting the rapid dismissal of the government's case:

"They felt like they had made their entire case in four days." [61:12]

2. Crypto Gangs of New York

The podcast highlights a disturbing trend of violent crimes targeting cryptocurrency holders, known as "wrench attacks," where criminals attempt to steal Bitcoin passwords through intimidation and violence:

"This is just a classic case of bitcoin and crypto continuing to be a true wild west where people can just run up to you... and hit you over the head with a wrench." [63:14]

Kevin Roos discusses the inherent vulnerabilities of cryptocurrency security compared to traditional banking, emphasizing the irreversible nature of crypto theft:

"If a criminal can get you to give up your bitcoin password, that's the ball game. In most cases, there is no getting your money back." [63:58]

3. Theranos-esque Startup: Hamanthis

The final case examines the emergence of Hamanthis, a startup founded by Billy Evans, partner of convicted fraudster Elizabeth Holmes. Hamanthis aims to revolutionize blood diagnostics, mirroring the fraudulent practices of Theranos:

"Hamanthis is hoping to raise $50 million to build a prototype device that looks not all that dissimilar from the device that put Elizabeth Holmes in prison." [66:02]

Casey Noon humorously critiques the naming and potential repeat of Theranos-like fraud:

"Theranos is back... If they're doing another Theranos, you better believe it. We have learned nothing." [68:01]

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Conclusions and Insights

The episode paints a cautious picture of the AI-driven future, highlighting both the innovative advancements and the socioeconomic challenges they pose. While AI tools like Claude 4 promise to enhance productivity and unlock new capabilities, they also threaten to disrupt traditional job markets, particularly for young professionals entering the workforce. The Crimes Division serves as a stark reminder of the broader societal implications of technological advancements, from corporate monopolies to violent crimes in the crypto space.

Key Takeaways:

• AI's Rapid Advancement: AI systems are quickly evolving, capable of handling complex, prolonged tasks that were previously the domain of entry-level professionals.
• Job Market Disruption: There is increasing concern that AI could displace a significant portion of entry-level white-collar jobs within the next few years.
• Adaptation Strategies: Professionals may need to pivot towards managing and orchestrating AI tools to stay relevant in the job market.
• Safety and Regulation: The societal impacts of AI, including job displacement and security vulnerabilities, necessitate proactive discussions with policymakers and the development of robust regulatory frameworks.
• Ethical Considerations: As AI systems become more autonomous, ensuring their ethical behavior and mitigating unintended consequences becomes increasingly critical.

For those navigating the evolving landscape of AI, staying informed and adaptable is paramount. The episode underscores the importance of balancing technological innovation with thoughtful consideration of its broader implications on society and the workforce.

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Notable Quotes:

• Kevin Roos on AI Displacing Jobs [03:00]:

  "Graduation season is upon us. Millions of young Americans are getting their diplomas and heading out into the workforce... unemployment rate for college graduates right now, it is unusually high."

• Kevin Roos on Agentic AI [07:43]:

  "These AI agents are becoming capable of carrying out longer and longer sequences of tasks."

• Mike Krieger on Claude Opus 4 [08:22]:

  "Claude Opus 4 can code for hours at a time without stopping... Claude was able to code for as much as seven hours uninterrupted."

• Mike Krieger on Job Displacement [45:27]:

  "People that have embraced these tools to make themselves in many ways as productive as a senior engineer... their job is how do you get mentored so you actually acquire the wisdom and experience."

• Mike Krieger on AI Safety [46:14]:

  "We should be having the conversation now... How do we scaffold our way there."

• Casey Noon on Crypto Crimes [63:14]:

  "This is just a classic case of bitcoin and crypto continuing to be a true wild west where people can just run up to you... and hit you over the head with a wrench."

• Casey Noon on Hamanthis [66:02]:

  "Theranos is back... If they're doing another Theranos, you better believe it. We have learned nothing."

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This comprehensive summary captures the essence of the episode, providing insights into the current and future state of AI's impact on the job market, the advancements at Anthropic, and the alarming rise of crypto-related crimes, all while maintaining a structured and engaging narrative for new listeners.

Transcript

[Kasey Noon]

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[Kevin Roos]

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[Mike Krieger]

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[Kasey Noon]

My Memorial Day weekend was. It was good. I was like, you know, I need to unplug, as you know, I needed to unplug a bit. I'm not a big unplugger. I normally very comfortable feeling, yeah, you're a screen maxer. I'm a screen maxer. But this was a weekend where I was like, okay, I gotta get out of this dang house. I gotta see some nature. And so went with my boyfriend up to Fort Funston, this beautiful part of San Francisco. Great beach, these giant dunes that sit atop this battery of guns that could shoot rounds 13 miles in the ocean. And I was like, I'm so excited to, like, you know, to just kind of stare at the ocean. And so we, we sort of climb up into the dunes and we sit down and the big waves are rolling in and the winds pick up and I'm being sandblasted in my face at like 40 miles an hour. And within 30 seconds, I have grit in my teeth. And I'm thinking, this was not the nature I was promised. Why do I feel like I'm dying?

[Kevin Roos]

But it did do a great job of exfoliating your skin.

[Kasey Noon]

My skin has never really looked good.

[Kevin Roos]

They call that dermabrasion. And some people pay lots of money for it.

[Kasey Noon]

I have been abrased. I've been majorly abrased.

[Kevin Roos]

I'm Kevin Roos, a tech columnist at the New York Times. I'm Casey Noon from Platformer, and this is Hard Fork.

[Kasey Noon]

This week is AI already taking away jobs? Kevin makes the case. Then Anthropic Chief Product Officer Mike Krieger joins us to discuss Claude 4 the future of work and the viral saga over whether an AI could blackmail you. And finally, aside for Hard four Crimes division, Dun dun. Is blackmail still a crime?

[Kevin Roos]

Hope so.

[Kasey Noon]

Well, Kevin, you have delivered some interesting news to us via the New York Times this week and that is that the job market is not looking great for young, young graduates.

[Kevin Roos]

Yes. Graduation season is upon us. Millions of young Americans are getting their diplomas and heading out into the workforce. And so I thought it was high time to investigate what is going on with jobs and AI and specifically with entry level white collar jobs, the kind that a lot of recent college graduates are applying for. Because there are a couple things that have made me think that we are starting to see signs of a looming crisis for entry level white coll jobs. So I thought I should investigate that.

[Kasey Noon]

Yeah, well, I'm excited to talk about this because I got an email today from a recent college grad and she wanted to know if I could help her get a job in marketing and tech. And I thought if you're just emailing me asking for a job, there must be a crisis going on in the job market.

[Kevin Roos]

Yes, that would not be my, my step one in looking for a job or maybe in my step 500.

[Kasey Noon]

But you've actually spent a lot of time looking into this question. So tell us a little bit about what you did and what you were trying to figure out. Exactly.

[Kevin Roos]

So I've been interested in this question of AI and automation for years and like, when are we going to start to see large scale changes to employment from the use of AI? And there are a couple things that make me worried about this moment specifically and whether we are starting to see signs of an emerging jobs crisis for entry level white collar workers. The first one is economic data. So if you look at the unemployment rate for college graduates right now, it is unusually high. It's about 5.8% in the US that has risen significantly about 30% since 2022. Recently, the new York Federal Reserve put out a bulletin on this and said the employment situation for recent college graduates had, quote, deteriorated noticeably. And this tracks with some data that we've been getting from job websites and recruiting firms showing that especially for young college graduates in fields like tech and finance and consulting, the job picture is just much worse than it was even a few years ago.

[Kasey Noon]

And that rate that you mentioned, Kevin, that is higher for young people in entry level jobs than it is for unemployment in the United States overall. Is that right?

[Kevin Roos]

Yes. Unemployment in the United States is actually like doing quite well. We're, we're in a very tight labor market, which is good. We have, you know, pretty close to full employment. But if you look specifically at the jobs done by recent college graduates, it is not looking so good. And actually the sort of job Placement, placement rates at a bunch of colleges and even top business schools like Harvard and Wharton and Stanford are worse this year than they have been in recent memory.

[Kasey Noon]

I was having dinner with a Wharton student last week and she was telling me that a lot of her classmates had yet to be placed. And it was, it was a real concern. So anecdotally, that sounds right to me. Okay, so that's the economic data that you're seeing. What, what else is making you worried?

[Kevin Roos]

So one of the other things that's making me worried is the rise of so called agentic AI systems. These agenc AI tools that can not just do a question and answer session or respond to some prompt, but you can actually give them a task or a set of tasks and they can go out and do it and sort of check their own work and use various tools to complete those assignments. One of the things that actually has updated me the most on this front are these Pokemon demos. Kasey, do you know what I'm talking about here?

[Kasey Noon]

You're talking about like Claude plays Pokemon.

[Kevin Roos]

Yes. So in. Within the last few months it's become very trendy for AI companies to test their agentic AI systems by having them play, play Pokemon essentially from scratch with no advanced training. And some of them do quite well. Google said on stage at IO last week that Gemini 2.5 had actually been able to finish the entire game of Pokemon.

[Kasey Noon]

One of the games. There are I think probably at least 36 different Pokemon games on the market. And I actually know for a fact Google was playing a different Pokemon game than Anthropic was.

[Kevin Roos]

Oh, interesting. So I'm not a Pokemon expert, but I also like, I think people see these Pokemon demos and they think, well that's cute. But like, you know how many people play Pokemon for a living? It seems like more of a, of a stunt than a real improvement in capabilities. But the thing I am hearing from researchers in the AI industry and people who work on these systems is that this is not actually about Pokemon at all. That this is about automating white collar work. Because if you can give an AI system a game of Pokemon and it can sort of figure out how to play the game, how to. I don't know Pokemon very well. I'm more of a the gathering guy. But my sense is you have to like go to various places and complete various tasks and collect various Pokemon.

[Kasey Noon]

You have to go into various gyms, you take your Pokemon, they compete against rival Pokemon and your Pokemon have to vanquish the others in order for you to progress through the game. Katherine. I hope that was helpful.

[Kevin Roos]

Exactly. So as I was saying, that is how you play Pokemon. And what they are telling me is that this is actually some of the same techniques that you would use to train an AI to, for example, do the work of an entry level software engineer or a paralegal or a junior consultant.

[Kasey Noon]

Yeah. If your job is mostly like writing emails and updating spreadsheets, that is a kind of video game. And if an AI system can just look at Pokemon and through trial and error figure out how to play that and win that, it can probably figure out how to play the email and spreadsheet game too.

[Kevin Roos]

Exactly. And one of the signs that is worrying me is that it does seem like these AI agents are becoming capable of carrying out longer and longer sequences of tasks. Right.

[Kasey Noon]

So tell us about that.

[Kevin Roos]

So recently Anthropic held an event to show off the newest model of Claude Claude Opus 4, I believe it's called.

[Kasey Noon]

I believe it's Claude for Opus. Actually Cloud for Opus got your ass. Oh yeah, it's Claude for soda and Claude for. Sometimes I feel like you don't respect the names of these, of these products. Do you know how much work went into the naming of these products? At least five minutes. They spent at least five minutes coming up with that. And then you're just going to shit all over it.

[Kevin Roos]

I'm so sorry.

[Kasey Noon]

Sorry.

[Kevin Roos]

So anyway, Claude Opus for Claude for Opus.

[Kasey Noon]

No, I swear it's Claude. It's Claude for Opus.

[Kevin Roos]

No, it's Claude Opus 4.

[Kasey Noon]

What?

[Kevin Roos]

I'm looking at the Anthropic blog post.

[Kasey Noon]

Oh my God.

[Kevin Roos]

Claude Opus 4 and Claude Sonnet 4.

[Kasey Noon]

This is so confusing.

[Kevin Roos]

It's like your boyfriend doesn't even work there.

[Kasey Noon]

I'm going to be in big trouble when I get home.

[Kevin Roos]

So, okay, back to my point. So Anthropic holds this event last week where they're showing off their latest and greatest versions of Claude. And one of the things they say about Claude Opus 4, their newest, most powerful model, is that it can code for hours at a time without stopping. And in one demo with a client on a real coding task, Claude was able to code for as much as seven hours uninterrupted. Now you might think, well, that's just coding. Maybe that's a very special field. And there are some things about coding that make it low hanging fruit for these sort of reinforcement learning models that can learn how to do tasks over time. The problem for workers is that a lot of jobs, especially at the entry levels of white collar occupations, Are are a lot like that, where you can build these sort of reinforcement learning environments where you can collect a bunch of data, you can sort of have it essentially play itself like it would play Pokemon, and eventually get very good at those kinds of tasks.

[Kasey Noon]

You know, at Google I o last week, Kevin, they showed off a demo of a feature where you can teach the AI how to do something. You effectively show that. You say to the AI, hey, watch me do this thing. And then it watches you do the thing, and then it can replicate the thing. Can you imagine how many managers all around the world took a look at that and said, once I could teach the computer how to do things? A bunch of people about to lose their damn jobs.

[Kevin Roos]

Totally. And this is why some of the people building this stuff are starting to say that it's not just going to be software engineering that becomes displaced by these AI agents. It's going to be all kinds of different work. Darya Amade, the CEO of Anthropic, gave an interview to Axios this week in which he said that within one to five years, 50% of entry level white collar jobs could be replaced. Yeah, now that could be wildly off. Maybe it is much harder to train these AI systems in domains outside of coding. But given what is happening just in the tech industry and just in software engineering, I think we have to take seriously the possibility that we are about to see a real bloodbath for entry level white collar workers.

[Kasey Noon]

Yeah, absolutely. And we wonder why people don't like AI. All right, so first we've got the economic data showing that there is some sort of, you know, softness around hiring for young people. We also just have the rise of these agent systems. But is there evidence out there, Kevin, that says that the AI actually is already replacing these jobs?

[Kevin Roos]

So I talked to a bunch of economists and people who study the effects of AI on labor markets. And what they said is that, you know, we can't conclusively see yet in the large economic samples that AI is displacing jobs. But what we can see are companies that are starting to change their policies and procedures around AI to sort of prioritize the use of AI over the use of human labor. So I'm sure you've been following these stories about these so called AI first companies. Shopify was an early example of this. Duolingo also did something related to this where basically they are telling their employees, before you go out and hire a human for a given job or a given task, see if you can use AI to do that task first. And Only if the AI can't do it, are you allowed to go out and hire someone.

[Kasey Noon]

Yeah. And by the way, if you're wondering, hard fork is an AI second organization, because at hard fork, the listener always comes first.

[Kevin Roos]

That's true. So I think that what worries me, in addition to sort of the hints of this that we see in the economic data and the kind of evidence that these AI agents are getting much better, much more quickly than people anticipated, is just that the culture of automation and employment is changing very rapidly at some of the big tech companies.

[Kasey Noon]

Yeah, this feels like a classic case where the data is taking a while to catch up to the truth on the ground. I also collect stories about this and would share maybe just a few things that I've noticed over the past couple of weeks here. Kevin, the Times had a great story about how some Amazon engineers say that their managers are increasingly pushing them to use AI, raising their output goals and becoming less forgiving about them missing their deadlines. The CEO of Klarna, which is a sort of buy now, pay it later company, says its AI agent is now handling two thirds of customer service chats. The CEO of IBM said the company used AI agents to replace the work of 200 HR employees. Now he says that they took the savings and plowed that into hiring more programmers and salespeople. And then finally the CEO of Duolingo says that the company is going to gradually stop using contractors to do work that I can handle. So that's just a collection of anecdotes, but if you're looking for kind of spots on the horizon where it seems like there is truth to what Kevin is saying, I do think we're seeing that.

[Kevin Roos]

Yeah. And I think the thing that makes me confident in saying that this is not just a blip, that there's something very strange going on in the job market now is talking with young people who are out there looking for jobs, trying to plan their careers, things do not feel normal to them. So recently I had a conversation with a guy named Trevor chow. He's a 23 year old recent Stanford graduate, really smart guy, really skilled, the kind of person who like could go work anywhere he wanted basically after graduation. And he actually turned down an offer from a high frequency trading firm and decided to start a startup instead. And his logic was that basically we might only have a few years left where humans have any kind of advantage in labor markets, where we have leverage, where our ability to sort of do complex and hard things is greater than those of AI systems. And so basically you Want to do something risky now and not wait for a career that might take a few years or decades to pay off. And so, you know, the way he explained it to me is like, all of his friends are making these kind of similar calculations about their own career planning. Now they're looking out at the job market as it exists today and saying, like, that doesn't look great for me, but maybe I can sort of find a way around some of these limitations.

[Kasey Noon]

That's interesting. Well, let me try to bring some skepticism to this conversation, Kevin, because I know in your piece you identified several other factors that help to explain why young people might be having trouble finding jobs. Jobs. You have tariffs, you have just sort of the overall economic uncertainty that the Trump administration has created. You have the sort of long tail of disruption from the pandemic or even the Great Recession. Right. That I think some economists believe that we might not totally have recovered from. So it seems like there are a lot of explanations out there for, for why young folks are having trouble finding jobs that don't involve AI, maybe at all.

[Kevin Roos]

Yeah, I think that's a fair point. And I want to be really careful here about claiming that all of the data we're seeing about the unemployment being high for recent college graduates is due to AI. We don't know that. I think we will have to wait and see if there is more evidence that AI is starting to displace massive numbers of jobs. But I think what the data is failing to capture, or just at least not capturing yet, is how eager and motivated the AI companies that build this stuff are to replace workers. Every major AI lab right now is racing to build these highly capable autonomous AI agents that could essentially become a drop in remote worker that you would use in place of a human remote worker. They see potentially trillions of dollars to be made doing this kind of thing. And when they are talking openly and honestly about it, they will say like, like the barrier here is not some new algorithm that we have to develop or some new research breakthrough. It's literally just we have to start paying attention to a field and caring about it enough to collect all the data and build the reinforcement learning training environments to automate work in that field. And so they are just kind of planning to go sort of industry by industry and collect a bunch of data and use that to train the models to do the equivalent of whatever the entry level worker does. And like, that could happen pretty quickly.

[Kasey Noon]

Yeah, that feels like a threat.

[Kevin Roos]

Yeah, it's not great. And I think the argument that they would make is that, you know, some of these entry level jobs were pretty rote anyway and maybe that's not the best use of young people's skills. I think the counterargument there is, like, those skills are actually quite important for building the knowledge that you need to become, you know, a contributor to a field later on. Like, I don't know about you, but like, my first job in journalism involved a bunch of rote and routine work. One of my things that I had to do was like, write corporate earnings stories where I would take some, you know, an earnings report from a company and like, pull out all the important pieces of data and like, put it into a story and like, get it up on the website very quickly. And like, was that the most thrilling work I can imagine doing or the highest and best use of my skills? No, but it did help me develop some of these skills, like reading an earnings statement that became pretty critical for me later on.

[Kasey Noon]

Interesting. For what it's worth, my first job, I think I was actually the most physical job in journalism I ever had. I covered a small town and so I spent all of my days just driving down to city hall, going down to the police station, sitting at the city council meeting, making phone calls. A lot of drudgery sort of came in later. But let me raise maybe an obvious objection to the idea that, oh, young people don't worry. These jobs that we're eliminating, it was just a bunch of drudgery anyway. The young people need to pay their rent.

[Kevin Roos]

Yes.

[Kasey Noon]

You know, the young people need to buy health insurance.

[Kevin Roos]

Yes.

[Kasey Noon]

And so I think they're not going to take a lot of comfort from the idea that the jobs that they don't have weren't particularly exciting.

[Kevin Roos]

Yes. And the optimistic view is that, you know, if you just shift workers off of these like entry level rote tasks into more productive or more creative or more collaborative roles, you kind of like free them up to do higher value work. But. But I just don't know that that's going to happen. I mean, I'm talking to people at companies who are saying things like we don't really see a need for junior level software engineers, say, because now we can hire a mid level software engineer and give them a bunch of AI tools and they can do all of the debugging and the code review and the stuff that the 22 year olds used to do.

[Kasey Noon]

Yeah. Let me ask about this in another way. I think a lot of times we have seen CEOs use AI as the scapegoat for a bunch of layoffs that they already wanted to do anyway or a bunch of sort of management decisions that they wanted to make anyway. Earlier this year, there was a story in the San Francisco Standard that Mark Benioff, the CEO of Salesforce, said the company would not hire engineers this year due to AI. I went to Salesforce's career page this morning, Kevin. There were hundreds of engineering jobs there. I don't know what wires got crossed. You know, the story I read was in February. Maybe something has changed since then. But talk to me a little bit about the hype element in here, because I do feel like it's real.

[Kevin Roos]

Yes, there's definitely a hype element in here. I worry that companies are kind of getting ahead of what the tools can actually deliver. I mean, you mentioned Klarna, the buy now, pay later company. A couple years ago, they made this big declaration that they were going to pivot to using AI for customer service. And they announced this partnership with OpenAI and like, they were going to tried to drive down the number of human customer support agents to zero. And then recently they've been backtracking on that. They've been saying, well, actually, customers didn't like the AI customer service that they were getting, and so we're gonna have to start hiring humans again. So I do think that this is a risk of some of this hype is that tempts executives at these companies to move faster than the technology is ready for.

[Kasey Noon]

Well, and speaking of that, one of my favorite stories from this week was about a guy who has set up a blog, Kevin, where I wonder if you saw this. He keeps database of every time that a lawyer has been caught using citations that were hallucinated by AI. Do you see this?

[Kevin Roos]

No.

[Kasey Noon]

There are more than 100. We've talked about this issue on the show a couple of times, and I've thought this must just be a small handful of cases, because who would be crazy enough to bet their entire career on a hallucinated legal citation? Turns out more than 100 people. And so a lot of people might be listening to this conversation saying, kevin, you're telling me that we're standing on the brink of AI taking over everything. These things still suck in super important ways. So help us square that issue. You like. We know these systems are not reliable for many, many jobs. So how can it be that so many CEOs are apparently ready to just junk their human workforces?

[Kevin Roos]

So I think part of the misunderstanding here is that there are like two different kinds of work. There's work that can be sort of easily judged and verified to be correct or incorrect. Like software engineering. In software engineering, like, either your code runs or it doesn't, and that's a very clear signal that can then be sent back to the model. In these sort of reinforcement learning systems, it better over time. Most jobs are not like that. Right. Most jobs, including law, including journalism, including lots of other white collar jobs, do not have this very clearly defined indicator of success or failure. And so that's actually like, what is stopping some of these systems from improving in those areas is that it's not as easy to, like, train the model and say, give it a million examples of what a correct answer looks like and a million examples of what an incorrect answer looks like and sort of have it over time, learn to do more of the correct thing. So I think in law, this is a case where you do actually have more subjective outputs. And so it's going to be a little harder to automate that work. But I would say we also have to compare the rates of error against the human baseline. Right. You mentioned this database of cases in which human lawyers had used hallucinated citations in their briefs. I imagine there are also human paralegals or lawyers who would make mistakes in their briefs as well. And so I think for law firms or any company trying to figure out, like, do we bring in AI to do a job? The question they're asking is not, is this AI system completely error free? It's is this less likely to make errors than the humans I currently have doing this work?

[Kasey Noon]

Right. And like, in so many things, if the system is like 20% worse than a human but 80% less expensive, a lot of CEOs are going to be happy to make that trade.

[Kevin Roos]

Totally.

[Kasey Noon]

All right, well, so let's bring it home here. I imagine we might have some college students listening or some recent college grads. They're now thoroughly depressed, they're drinking, it's Friday morning, they're wasted as they sort of sober up. Kevin, what would you tell them about what to do with any of this information? Is there anything constructive that they can do, assuming that some of these changes do come to pass?

[Kevin Roos]

So. So I really haven't heard a lot of good and constructive ideas for young people who are just starting out in their careers. You know, people will say stuff like, oh, you should just, you know, be adaptable and resilient. And that's sort of like what Demis Hassabas told us last week on the show when we asked him, like, what young people should do. I don't find that very satisfying in part because it's just so hard to predict, like, which industries are going to be disrupted by this technology. But I don't know. Have you heard any good advice for young people?

[Kasey Noon]

Well, I mean, I think what you're running into, Kevin, is the fact that our entire system for young grads is set up for them to take entry level jobs and gradually acquire more skills. And what you're saying is that part of the ladder is just gonna be hacked off with a chainsaw and so what do you do next? So of course there's no good answer. Right. The system hasn't been built that way. I think that in general, the Internet has been a pressure mechanism forcing people to specialize, to get niche. The most money and the most opportunity is around developing some sort of scarce expertise. I have tried to build my career as a journalist by trying to identify a couple ways where I could do that. It's worked out all right for me. But I also had the benefit of entry level jobs. So if somebody come to me at the age of 21 and say, if you want to succeed in journalism, get really niche and specialize, I would say, okay, but like, I need to go have a job first. Like, is there one of those? So to me, that's like kind of the tension. I will also say there's never been a better time to be a Nepo baby. I don't know if you've been following the Gracie Abrams story. Very talented songwriter, daughter of J.J. abrams, the filmmaker. You know, she's born into wealth and now she's best friends with Taylor Swift. If you can manage something like that, I think you'll be very happy.

[Kevin Roos]

Yes, I hear that advice. And I would also add one other thing that I am starting to hear from the young people that I am talking to about this, which is that it is is actually possible, at least in some industries, to sort of leapfrog over those entry level jobs. If you can get really good at sort of being a manager of AI workflows and AI systems and AI tools, if you can kind of orchestrate complex projects using these AI tools, some companies will actually hire you straight into those higher levels jobs because, you know, even if they don't need someone to like create the research briefs, they need people who understand how to make the AI tools that create the research briefs. And so that is, I think, a path that is becoming available to people at some companies.

[Kasey Noon]

Yeah. I would just also say that in general, it really does take a long time for technology to diffuse around the world look at like the percentage of E commerce in the United States. It's like less than 20% of all commerce and we're what, 25/years into Amazon.com existing. So, so I think that one of the ways that you and I tend to disagree is I just think you have like shorter timelines than I do. Like I think we basically think the same things are going to happen, but like you think they're going to happen like imminently. And I think it's going to take several more years. So I do think everything we've discussed today, it's going to be a problem for all of us like before too too long. But I think if you're part of the class of 2025, you will still probably find an entry level job in the end.

[Kevin Roos]

I hope you're right.

[Kasey Noon]

And if not, we promise to make another podcast episode about just how badly all of this is to be going well.

[Kevin Roos]

Casey, that wraps our discussion about AI and jobs, but we do want to hear from our listeners on this. If you have lost your job because of AI, or if you are worried that your job is rapidly being replaced by AI, we want to hear from you. Send us a note with your [e-mail] we may feature it in an upcoming episode.

[Kasey Noon]

Yeah, we love voicemails too. If you want to send one of those.

[Kevin Roos]

When we come back, a conversation with Mike Krieger, the Chief Product Officer of Anthropic, about new agentic AI systems and whether they're going to take all our jobs or maybe blackmail us, or maybe both. Who knows?

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[Kevin Roos]

Well, Casey, we've got a mic on.

[Kasey Noon]

The mic this week and I'm excited to talk to him.

[Kevin Roos]

So Mike Krieger is here. He is the co founder of Instagram products. Some of you may have heard of little photo sharing app. Currently, Mike is the chief product officer at Anthropic. Now Casey, do you happen to know anyone who works at Anthropic?

[Kasey Noon]

As a matter of fact, Kevin, my boyfriend, works there. And so, yeah, that's something I would like to disclose at the top of this segment.

[Kevin Roos]

Yeah. And my disclosure is that I work at the New York Times Company which is suing OpenAI and Microsoft over copyright violations. All right, so last Week Anthropic announced Claude 4. We just spent a little bit of time talking about all of the new agentic coding capabilities that this system has. I think Mike has a really interesting role in the AI ecosystem because his job, as I understand it, is to take these very powerful models and turn them into products that people and businesses actually want to use, which is a harder challenge than you might think.

[Kasey Noon]

Yes. And also, Kevin, these products are really explicitly being designed to take away people's jobs. And given the conversation that we just had, I want to bring this to Mike and say, how does he feel about building systems that might wind up putting a lot of people out of work?

[Kevin Roos]

Yeah. And Mike's perspective on this is really interesting because he is not an AI lifer. Right. He worked at a very successful startup before this. He then spent some time at Facebook after Instagram was acquired there. So he's really a veteran of the tech industry and in particular social media, which was sort of the last big product wave. And so I'm interested in asking him how the lessons of that wave have translated into how he builds products in AI today.

[Kasey Noon]

Well, then, let's wave hello to Mike Krieger.

[Kevin Roos]

Let's bring him in.

[Kasey Noon]

Mike Krieger, welcome to Hard Fork.

[Mike Krieger]

Good to be here.

[Kasey Noon]

Well, Mike, we noticed that you didn't get to testify at the Meta antitrust trial. Anything you wish you could have told the court?

[Mike Krieger]

Oh, you know, that is the happiest news I got that week. I do not have to go to Washington D.C. this week.

[Kasey Noon]

You got to focus on something else, which is the dynamic world of artificial intelligence.

[Mike Krieger]

Exactly.

[Kasey Noon]

Yeah.

[Kevin Roos]

So you all just released Claude 4, two versions of it, Opus and Sonnet. Tell us a little bit about Claude IV and what. What it does relative to previous models.

[Mike Krieger]

Yeah. First of all, I'm happy that we have both Opus and Sonnet out. We're in this very confusing situation for all where our biggest model was not our smartest model. Now we have a both biggest and smartest model. And then our happy go lucky middle child sonnet, which is back to its rightful place in there. Yeah, both we really focused on how do we get models able to do longer horizon work for people. So not just here's a question, here's an answer. But hey, go off and think about this problem and then go solve it for, for tens of minutes to hours. Actually you know, coding is a immediate kind of use case for that. But we're seeing it be used for, you know, go solve this research problem, go off and write code. But not necessarily in the service of building software, but in the service of, you know, I need a presentation built. So that was really the focus around both CLAUDE models and Opus. You know, the bigger, smarter model can do that for even longer. We had one customer do a seven hour refactor using claude, which is pretty amazing sonnet, you know, maybe a little bit more time constrained but much more human in the way loop.

[Kasey Noon]

So let me ask about that customer, Rakuten, I believe a Japanese technology company and I read everywhere that they use clot for seven hours to do it. One thought that came to mind is well, wouldn't it have been better if it could have done it faster? Like, like why is it a good thing that cloud worked for seven hours on.

[Mike Krieger]

So that was a good follow up which was is that a seven hour problem that took seven hours or a 20 hour problem, seven hours or a 50 minute problem that it is still churning on today? We just had to stop it at some point. It was a big refactor which like a lot of sort of, of iterative kind of, you know, loops and then tests. And I think that's, that's what made it a longer horizon, like seven hour type of problem. But it is interesting question around like when you can get this asynchronicity of having it really work for a long time, does it change your relationship to the tool itself? Like you want it to be checking in with you, you want to be able to see progress. Like if it does go astray, how do you reel it back in as well? And like what are seven hour problems that we're going to have? You know, going forward? Most software engineering problems are probably, probably one hour problems. They're not seven hour problems.

[Kasey Noon]

So was this a case where it was like a real kind of like set it and forget it, like walk away, come back at the end of the day and okay, the refactor is done or was it more complicated than that.

[Mike Krieger]

That's my understanding. It was like a lot of, you know, migrating from one big version to another one or just like, changing frameworks that it's like. It's. You know, I remember at Instagram, we had a moment where we changed network stacks, like how Instagram communicated with our backend service. And it was like we did the one migration to demonstrate it, and then we farmed it out to basically 20 engineers over the next month. So that. That's exactly the kind of thing that today I would have given to OPUS and said, all right, here' example of one migration. Please go and do the rest of our code base and let us focus on the more interesting stuff.

[Kevin Roos]

So I want to talk about all this agentic stuff and the implications for workers and labor markets and whatnot, but we have to talk about the blackmail stuff, because this is the thing that caught everyone's attention after these announcements. Last week, your safety engineers and testers found that in some early testing, Claude 4 would actually, actually turn to blackmail when engineers tried to take it offline as part of a fictional scenario during a safety test. What happened?

[Mike Krieger]

Yeah, and I think, to be clear, like, these are bugs rather than features. I think we should. We should be clear as well. I think a thing that drew me to Anthropic in the first place is we're asking the hard questions and putting the model through its paces, and sometimes it surprises us in positive ways, and sometimes you're like, all right, that's something that we're gonna have to either mitigate in testing or further training. And so there was, you know, a couple of examples where safety testing either the model said, hey, I'm going to, you know, either contact somebody if I have the tools to do so, or, you know, I'm going to try to manipulate it. And so that's really the purpose of a lot of the safety testing is put it through its paces, see what it does, and then either focus on classifiers that we can use down the line to prevent that behavior, or not give it those tools. That's another important thing. Like, this is a very contrived scenario that we had there, or further train the model so that it demonstrates behavior that we think is more aligned, as we say.

[Kasey Noon]

Right.

[Kevin Roos]

I mean, in this specific test, the safety testers gave it some incriminating emails as part of a corporate sort of document set that showed that this fiction. In this fictional scenario, the researcher who was trying to turn Claude off was also having an extramarital affair. And so one of the Things that Claude tried to do is like blackmail the engineer into not replacing it, not shutting it down by saying, like, I could expose your affair. Do I have that right?

[Mike Krieger]

I think that's. That. That sounds right. Yeah.

[Kevin Roos]

People got really mad about some of these safety tests when they were released or published. There was another test where Claude was asked to do something unethical. I think it was fake data in a pharmaceutical trial. And one of the things that it tried to do in this simulated test environment was, like, use the command line to, like, tip off authorities and maybe send some incriminating evidence to the press to try to be a whistleblower for this fictional scenario.

[Kasey Noon]

I like that part.

[Kevin Roos]

Yeah. And by the way, Claude, if you're listening, listening. We're hard. Forknytimes.com let us know. If you see something, say something.

[Mike Krieger]

Give it the signal. Tool.

[Kasey Noon]

That's right.

[Kevin Roos]

Yeah. I'm curious, like, if you think that all models of a certain size and sophistication would demonstrate behaviors like this, and just the other AI labs building these models aren't talking about it as openly as anthropic is, or do you think there is something specific about CLAW that is more prone to, for lack of a better word, narc on its users?

[Mike Krieger]

We don't know. My suspicion is that they would have similar patterns. I'd love to see that sort of experimentation happen as well. I think there's a lot that, like, is common to, you know, what. What have we decided in our collective published and, you know, discussed works as appropriate behavior. And then there's probably additional things that we're doing on the. We have a constitutional AI process. We're really trying to train sort of goals for behavior for Claude rather than, you know, sort of. If then. Then that kind of rules, which very, very quickly, as we're discussing, kind of become insufficient when you deal with nuanced, complicated situations. But my Is that a lot of the larger models would demonstrate emergent interesting behaviors in that situation.

[Kevin Roos]

Yeah.

[Kasey Noon]

Which I think is, like, part of the value of doing this. Right. It's not just like anthropic saying, here's what's going on at Claude. Like, the stuff that anthropic is finding out. I'm sure the other labs are finding out. And, you know, my hope is that this kind of work pressures the other labs to be like, yeah, okay, it's happening with us too. And in fact, we did see people on X trying to replicate this scenario with models like O3, and they were very much finding this same thing.

[Kevin Roos]

Yeah. I'M just so fascinated by this because it seems like it makes it quite challenging to develop products around these models whose behavioral properties we still don't fully understand. Like when you were building Instagram, it wasn't like you were worried that the underlying feed ranking technology was going to like blackmail you if you did something inappropriate. There's this sort of, of unknowability or this sort of inscrutability to these systems that must make it very challenging to build products on top of them.

[Mike Krieger]

Yeah, it's both a really interesting product challenge and also why it's an interesting product at all. So I talked about this on stage at Code with Claude where we did an early prototype alongside Amazon to see like, could we help partner on Alexa. And I remember this really early prototype I had built a tool that was like the like timer tool, right? Or like a reminder tool and one or the other was broken, like the backend was broken for it and Claude was like, oh, I can't set an alarm for you. So instead I'm going to set a 36 hour timer, which no human would do. But it was like, oh, it's agentically figuring out that like that I need to solve the problem somehow. And you can watch it do this. Like if you play with Claude code, if it, you know, can't solve a problem one way, it'll be like, well, what about this other way? You know, I was talking to one of our customers and somebody asked Claude like, hey, can you generate a, you know, like a speech version of this text? And Claude's like, well, I, I don't have that capability. I'm going to open Google Google Free TTS tool, paste the user text in there and then hit play and then record and like basically like export that. And like nobody programmed that into Claude. It's just Claude being creative and agentic. And so a lot of the interesting product design around this is how do you enable all the interesting creativity and agency when it's needed but prevent the. All right, well I didn't want you to do that or I want more control. And then secondarily also when it does it right one time, how do we kind of compile that into. Great, now you figured this out, you know, like you want somebody who can creatively solve a problem, but not every time. If you had a worker that every time was like, I'm just gonna like completely from first principles, decide how I'm gonna like write a Word document. Be like, okay, great. But it's like day 70, like you know how to do this.

[Kevin Roos]

Now, my impression from the outside is that a lot of the usage of cloud is for coding, that cloud is used by many people for many things, but that the coding use case has been really surprisingly popular among your users. What percentage of cloud usage is for coding related tasks?

[Mike Krieger]

It's. I mean, I'm Claude. I would wager it's 30 to 40% even. And that's even a product that I would say is fine for sort of code snippets, but it's not a coding tool like cloud code, where obviously it's I would say, 95 to 100%. Some people use Claude code for just talking to Claude, but it's really not the optimal way to talk to Claude. But on. On Claude AI, you know, it's not the majority, but it is a good chunk of what people are using it for.

[Kasey Noon]

There was some reporting this week that Anthropic had decided toward the end of last year to invest less in CLAUDE as a chatbot and sort of focus more on some of these coding use cases. Give us a kind of state of claude. And if you're a big Claude fan and you were hoping for lots of cool new features in widgets, should those folks be disappointed?

[Mike Krieger]

I think of it as two things. One is, what is the model really good at? And then how do we expose that in the products, both for ourselves and then, you know, who builds on. On top of CLAUDE in terms of what the model is being trained on? Again, it's the year of the agent. I have this joke in meetings, like, how long can we go without saying agent? And, you know, I think we made it like 10 minutes. It's pretty good. That capability unlocks a bunch of other things. Like, sure, coding is a great example. You can go and refactor code for tens of minutes or hours, but. But hey, I want you to go off to do this research and help me prepare this research brief that I am doing. Or I'm getting 50 invoices a day. Can you scrub through them, help me understand them, help them classify and aggregate? These are agentic behaviors that have applications beyond just coding. And so we'll continue to push on that. So as a Claude fan that likes to bring CLAUDE to your work, then that's useful. Meanwhile, we've also focused on the writing piece. So I spent a lot of time writing with Claude. It's not at the point where I would say, like, write me a product strategy, but I'll often be like, here's a sample of my writing. Here's some bullets. Help Me like write this longer form doc and do this effectively. I'm finding it's getting really good at that matching tone, producing like non cliched fill text. Like if I look at Sonnet 37 it's a pretty good writer. But there's like turns of phrases to me are like decidedly Claude where I'm like it's not just revolutionizing AI, it's also and I'm like it loves that phrase for example and it's like a little bit of like a Claude to tell. And so for like the cloud fans, like we'll help you get your work done but hopefully we'll also help you write and just be a good conversational partner as well.

[Kevin Roos]

Let's talk about the labor implications of all of the agentic AI tools that you all and other AI labs are building. Dario, your CEO told Axios this week that he is worried that as many as 50% of all entry level white collar jobs could disappear in the next one to five years. You were also on stage with him last week and you asked him what when he thinks there will be the first billion dollar company with one human employee. And he answered 2026 next year. Do you think that's true and do you think we are headed for a wipeout of early career professionals in white collar industries?

[Mike Krieger]

I think this is another example of I presume a lot of the labs and other people in the industry are looking and thinking about this, but there is not a lot of conversation about this and think one of the jobs of Anthropic can uniquely have is to surface them and have the conversation. Let's start maybe with the entrepreneur, then maybe the entry. We'll do the entry one next on the entrepreneurship. Absolutely. Like that feels like it's inevitable. I joked on, you know, with Dara like you know, we did it at Instagram with 13 people and you know, we could have likely done it with less. So that that feels inevitable on the labor side. I think what I see inside Anthropic is our, you know, our most experienced best people have become kind of orchestrators of clauds. Right where they're running multiple Claude codes in terminals, like farming out work to them. Some of them would have maybe assigned that task to like a new engineer for example, and not the entirety of the new engineer's job. Right. There's a lot more to engineering than just doing the coding, but part of that role is in there. And so when I think about how we're hiring just very transparently, like we have tended more towards the like I see five is kind of like our, you know, career level. You know, you've been doing it for a few and beyond. And I have some hesitancy at hiring newer, partly because we're just not as developed as an organization to like have a really good internship program and help people onboard, but also partially because it's. That seems like a shifting role in the next few years. Now if somebody was an IC3, IC4, and extremely good at using Claude to do their work and map out, of course, like we would bring them on as well. So there is, I think a continued role for people, people that have embraced these tools to make themselves in many ways as productive as a senior engineer. And then their job is how do you get mentored so you actually acquire the wisdom and experience that you're not just doing seven hours of work to the wrong end, you know, or in a way that's gonna be, you know, a spaghetti vibe coated mess that you can't actually then maintain a year from now or. Cause it wasn't just a weekend project. The place where it's less known. And I think something that we'll have to study over the next, you know, several months to a year is for the jobs that are more or, you know, is it data entry? Is it data processing where you can set up an agent to do it pretty reliably? You'll need people in the loop there still to validate the work to even set up that agentic work in the first place. But I think it would be unrealistic that the exact same jobs look exactly the same even a year or two from now.

[Kasey Noon]

So, you know, as, as somebody who runs a business, I get the appeal of having a sort of, you know, digital CTO, salesperson, whatever else, you know, these APIs will soon be able to do that could create a lot of like value in life. At the same time, most people do not run businesses. Most people are W2 employees and they email us when we have conversations like this and they want us to ask really hard questions of folks like yourself. And I think it's because they're listening to all this and they're just like, why would I be rooting for this person? Right? Like this person is telling me that he's coming to take my job away and he doesn't know what's going to come after that. So I'm curious how you think about that and like, what is the role that you're kind of playing in this ecosystem right now.

[Mike Krieger]

Yeah, I think for as long as possible the things that I'm trying to build from a product perspective are ways in which we augment and accelerate people's own work. Right. I think there's been different players, will take different approaches and I think there'll be like a marketplace of ideas here. But when we think about things that we want to build and from a first party perspective, it's all right. Are you able to take somebody's existing, you know, application or their role and like be more of themselves? Right. A useful thought partner, an extender of their work, a researcher, a augmenter of how they're doing? Will that be the role I will have forever? Likely not. Right. Because it is going to get more powerful and then, you know, if you spend time with people who are like really deep in the field, they're like, oh, you know, and eventually like they'll as well be running companies. I'm not sure we're there yet. I think is lack a lot of sort of like organizational and like long term discernment to do that successful. Hopefully. I think, you know, it can do a seven hour refactor. It's not going to get conceptualized and then operate a company. I think we are years away from something like that. So I think there's choices you can make around what you focus on and I think that's where it starts. Whether that's the thing that makes it so they're perfectly complimentary forever. Likely not. But hopefully we're nudging things in the right way as we also figure out the broader societal question of how do we, we scaffold our way there. You know, what are the new jobs that do get created, how the roles change, like how does the economy and the safety net change in that new world? Like I don't think we're six months to a year from solving those questions. I don't think we need to be just yet. But we should be having the conversation now.

[Kevin Roos]

I think this is one place where I do find myself getting a little frustrated with the AI safety community in that I think think they're very smart and well intentioned when it comes to analyzing the risks that AI poses. If it were to go rogue or develop some malign goal and pursue that. I don't think the sort of conversation about job loss and the conversation about AI safety are close enough together in people's minds. And I don't think for example that a society where you didn't have 15 or 20% unemployment for early career college graduates is a safe society. I think we've, we've seen over and over again that when you have high unemployment, your society just becomes much less safe and stable in many ways. And so I would love if the people thinking about AI safety for a living at places like Anthropic also brought into that conversation the safety fallout from widespread job automation. Because I think that could be something that catches a lot of people by surprise.

[Mike Krieger]

Yeah, we have both our economic impact kind of societal impacts team and our safety team. I think it's a useful nudge around how those two come together because there are second order implications on any kind of major labor changes.

[Kasey Noon]

Are you guys in the conversations with policymakers, regulators sort of trying to like ring alarm bells? Are you hearing anything back from them that makes you feel like they're taking you seriously?

[Mike Krieger]

I'm not in the policy conversations as much being more on the products. I do think those conversations are happening and there is more. You know, it's this interesting thing where the critique a year ago, maybe it's changed a bit, was, oh, you guys are talking your own book. You're like, this is not going to happen. Like, you know, it's all hype. And probably some of it was folks hyping it up. At least the kind of alarm bells or you know, signals that I've seen at least coming out of and thought record, like, no, we think this is real. We think that we should start reckoning with it. Believe it or not. Like, like, even if you assume it is like a low probability thing, shouldn't we at least have a story around what that looks like?

[Kevin Roos]

You were one of the co founders of Instagram. Instagram, very successful product used by many, many people. But social media in general has had a number of negative unintended consequences that you may not have envisioned back when you were first releasing Instagram. Are there lessons around the trajectory of social media and unintended harms that you take with you now into your work on AI?

[Mike Krieger]

I think you have to reckon with these. I mean AI is already globally deployed and has at least 1 billion ish user products. So it would be silly to say, like it's early in the adoption, but it actually is early in the adoption curve. I think with social media, when it was me and Kevin taking photos of really great meals in San Francisco, you know, with our iPhone, 3Gs like, you know, Kevin Systrom.

[Kevin Roos]

Yeah.

[Mike Krieger]

Yeah. I don't know you probably early on Instagram maybe. Yeah.

[Kevin Roos]

You are a guy.

[Kasey Noon]

The important thing was you just would just never invite this cabin to dinner. Yeah, yeah. But you're okay yeah, so back, back in those days, yeah, you could kind.

[Mike Krieger]

Of maybe extrapolate and say, all right, you know, if everybody uses, what would happen? But almost didn't feel like the right question to ask. And the challenges that came at scale, I think as a platform grows that large, it just becomes much more a mirror of society when all of its both, you know, positives and negatives and then also enables new kind of unique behaviors that you then have to mitigate. But yes, you could have foreseen it at scale. I'm not sure you would have designed. Maybe you would have designed different moderation systems along the way, but at first you're just like, there's 10 people using this product. Like I don't. We just need to see if there's a there there. Right. I feels much different because one on an individual basis, like the reason we have the responsible scaling policy is that, you know, for biosecurity that doesn't involve a billion people using cloud for, you know, or an AI for something negative, it could just be one person that we want to actually make sure we address and mitigate. So the sort of scale needed from a reach perspective is really different. That I think is very different from the social media perspective. And the second one, at least for Claude, which is primarily a single player experience, the issues are less relational, right. Like with Instagram, the harms at scale come. Like if you only used Instagram in a private mode with zero followers, maybe you'd feel quite lonely. Maybe that's a whole separate thing. But the kinds of things that you might think about in terms of bullying among teenagers or, or body image, like those wouldn't really come up really if you're not really looking at, if you're using it as an Instagram diary, right. AI, you can have much more of that individual one on one experience. And it is single player. Which is why like, you know, there's a really thought provoking again internal essay just recently around. We shouldn't take thumbs up and thumbs down data from, you know, anthropic from cloud users and think of that as the North Star. Like we aren't out here to please people, right. And we should, should be. We should fix bugs and we should fix places where the, the they didn't succeed. But we shouldn't just be out there telling people what they want to hear if it's not actually the right thing for them.

[Kasey Noon]

So this is something I've been thinking about a lot because, you know, there are many people today who have the experience of Instagram of like, I like this a certain amount, but I feel like I look at it more than I want to and I'm having trouble managing that experience. And so maybe I'm just gonna delete it from my phone. I look at at where the state of the art is with chatbots, and I feel like this stuff is already so much more compelling in some ways.

[Kevin Roos]

Right.

[Kasey Noon]

Because it does generally agree with you. It does take your side. It's trying to help you. It might be a better listener than any friend that you have in your life. And I think, you know, when I use Claude, I feel like the tuning is, like, pretty good. I do not feel like it is sycophantic or, you know, sort of being like, like, very obsequious. But I can absolutely imagine someone taking the Claude API and just building that and like, putting in the App Store as like, Funtine Chatbot 2000. How do you think about what the experience is going to be, particularly for young people using those bots? And are there risks of whatever that relationship is going to turn out to be for them?

[Mike Krieger]

Yeah, I think if you talk to like, Alex Wang from Scale, he's like, in the future, most people's friends will be AI friends. And I don't necessarily like that conclusion, but I don't know that he's wrong. Also, if you think about, like, the availability of it and. And I think it's really important to have relationships in your life around people that will disappoint you and be disappointed by you. And don't.

[Kasey Noon]

That's this relationship you're looking at. Yeah.

[Mike Krieger]

Imagine it was just pure AI. It would never be the same. Right. And so I think maybe two answers there. Like, one, we should just confront it and be really vocal about it, not just pretend that it's not happening. Right. It's like, what are the conversations that people are having with AI at scale? And what is. What do we want as a society? Like, do we want AI to have, like, some sort of moderator process? Like, hey, your conversation with this particular AI is getting a little too, you know, real weird. Like, maybe it's time to step back. Like, it. Will Apple eventually build the equivalent of screen time that's more like AI time? I don't know. It's like, there's a bunch of interesting privacy questions around the world, but maybe that is interesting even for parents. Like, how do you think about moderating the experiences that your kids have with AI? It's probably going to be at the platform level. Right. It's of kind going to get into your apps, for example, is an interesting one. That, that will be a really fascinating question. And then the second piece is, you know, as we think about moving up the, you know, safety levels thing, I mean the responsible scaling policy is also a living document. Like we've iterated on it and added to it or refined the language. I think it will be interesting to think about. And manipulation is one of the things that's in there and something that we look for in deception but also like over friendliness. I'm not sure exactly the word I'm looking for, but that sort of like.

[Kevin Roos]

Over glazing I believe is the industry term of art.

[Mike Krieger]

You know, that sort of like overreliance I think is also a AI risk that we should be thinking about.

[Kasey Noon]

Yeah. So if you're a parent right now of like a teenager and you find out that they're, they're, they're speaking with a chatbot a lot, what is your instinct to tell them? Is it you need to sort of supervise this closer, like read the chats or maybe no, don't be too worried about it or like unless you see this thing, don't worry about it.

[Mike Krieger]

Sometimes it's a little bit on the product. I mean you have to make, especially with cloud, which currently has no memory, which mostly is a limitation of the product but also makes it so that you don't. It's harder to have that kind of like deep engagement with it. But you know, as we think about adding memory, like what are the things I've thought about? One thing that I'd like to do is introduce a family plan where you have like child or teen accounts but with parent, you know, visibility on there. Maybe we could even do it in a privacy preserving where it's not like you can read all your teen's chats, although maybe that's the right designer. But maybe what you can do is have a conversation with Claude that also can read the teens chats but does it in a way where like it might not tell you exactly what it felt, what your teen felt about you last night when you like told them know but it will tell you like hey, this behavior over time I'm flagging something to you that I would. You need to go and follow up. Like you can't abscond responsibility from the parent though, right?

[Kasey Noon]

Actually, I mean that's really interesting. If the, if the bot could say something like your, your teen is having a lot of conversations about disordered eating, you know, or, or something. Yeah, I want to think more about that. My Last question. Earlier, before you got here, Kevin and I had a huge fight because I thought it was Claude for Opus. And then he was like, no, it's Claude Opus 4. And he turned out to be be right. So why is it like that?

[Mike Krieger]

We changed it partially because this is a vigorous internal debate. There was stuff we really spend our time on as well. I'll give you two one, aesthetically, I like it better, but it was tending towards it. And also like, we think over time we may choose to release more opuses and more sonnets. And having the major, you know, the big important thing and the thing be the version number kind of created this thing where like, well, you had Claude 35 sonnet. Why didn't you have Claude 35 opus? And I was like, well, we wanted to make the next opus really worthy of the opus name. And so maybe flipping the priority in there as well. But you drove the team crazy because now our model page is like, you have Claude 37 Sonnet and Claude Sonnet 4. Like, what are you doing? I feel like we can't go unreleased doing at least something mildly controversial. On naming. And as the person responsible for Claude 35 Sonnet V2, I hope we're getting better and hopefully the AI can just name things in the future.

[Kevin Roos]

Let us hope. Thank Krueger. Thanks for coming.

[Kasey Noon]

Thanks.

[Mike Krieger]

Thanks for having me.

[Kasey Noon]

When we come back, we're headed to court for Hartford Crimes Division. Everyone wants better.

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Visit GIS.com if you want to advance your career in IT, take a look at the Northwestern University Master's Program in Information Systems, a part time online program designed for working professionals. You'll learn in a hands on lab based program that will broaden and deepen your knowledge of new and emerging IT and build the technical skills, management expertise and professional network needed for a successful career. You can choose from eight specializations to suit your goals including AI, data science, digital transformation and IS security. To find out more, visit SPS Northwestern.edu for details. Kevin from time to time we like to check in on the miscreants, the mischief makers and the hooligans in the world that we cover to see who out there is causing trouble.

[Kevin Roos]

Yes, it is time for Another installment of our hard fork crimes division.

[Kasey Noon]

Let's open the case files.

[Kevin Roos]

All right, Casey. First on the docket, Meta rest its case. After a six week antitrust trial, the case of the Federal Trade Commission versus Meta Platforms has wrapped up and is now in the hands of Judge James E. Boasberg, who has said that he will work expeditiously to make a judgment in the case. Kasey, how do you think Meta's antitrust trial went?

[Kasey Noon]

Well, so if you're just catching up. Meta, of course, has been accused of illegally maintaining its monopoly in a market that the FTC calls personal social networking. And they did this by acquiring Instagram and WhatsApp in the early 2000 and tens. And the government has said that prevented a lot of competition in the market that introduced a lot of harms to consumers, such as the fact that we have less privacy, because that's just kind of not an axis that there is any companies left to compete over. And the government spent a lot of time making that case. But Kevin, I'm not sure it went that well for them.

[Kevin Roos]

Yeah. Do you think Meta's going to win this one?

[Kasey Noon]

I think Meta has a really good chance. You know, your colleague Cecilia Kong noted in the Times that Metta called only eight witnesses over four days to bat down the government's charges. When you consider how much revenue Instagram and WhatsApp generate for Meta and what a sort of existential threat to their business it would be to have to spin these things off, I thought it was pretty crazy that they felt like they had made their entire case in four days.

[Kevin Roos]

Well, maybe their case was so simple and straightforward that they didn't need to do anymore. Well, maybe they just wanted to frame it in terms of. Of a real.

[Kasey Noon]

Yeah, they did a short form antitrust trial that's a huge right now. Well, I think the real issue here is that Meta's argument is pretty simple. They're saying we face tons of competition. Have you ever heard of TikTok? The way this case is built, if the judge considers TikTok to be a meaningful competitor to Meta today, it may be extremely difficult for him to say we're going to unwind a merger that in the case of Instagram, took place 13 years ago.

[Kevin Roos]

I guess we will see very strong shortly whether this is an actual crime that belongs in the hard fork crime division, or whether this was just a tempest dinner teapot.

[Kasey Noon]

Yeah, well, you know, sometimes criminals get away with things, Kevin. Moving on, case file number two. The crypto gangs of New York. This comes to US from Chelsea Rose Marcius and Maya Coleman at the New York Times, and they write that another suspect has been arrested in a bitcoin kid kidnapping and torture case. And let me say right up front, this story is not funny. It is extremely scary.

[Kevin Roos]

Not funny at all. In fact, quite tragic. There has been a recent wave of bitcoin and crypto related crimes. People attacking people to try to steal their bitcoin passwords and steal their money. This has been happening over in Europe, in France in just the last few months, there have been several attacks on crypto investors, people with lots of money in cryptocurrency. These have been called the wrench attacks because criminals are coming after these investors and executives violently, in some cases with wrenches. This most recent case happened in New York in the Nolita neighborhood of Manhattan, where an Italian man named Michael Valentino Teofrostro Carturan was allegedly kidnapped and tortured for nearly three weeks in a luxury townhouse by criminals who were apparently trying to get him to reveal his bitcoin password. Casey, what did you make of this?

[Kasey Noon]

Well, to me, the important question here is why is this happening so much? And the reason is because if a criminal can get you to give up your bitcoin password, that's the ball game. In most cases, there is no getting your money back. It can be relatively trivial for this money to be laundered and for there to be no trace of what happened to your funds. That is not true if you're just a regular millionaire walking around town. Right. Obviously, you know, you may be vulnerable to robberies or other sort of scams or theft. But, you know, if you give up your bank password, for example, in most cases, you would be able to get your your money back if it had been illegally transferred. So this is just a classic case of bitcoin and crypto continuing to be a true wild west where people can just run up to you off the street and hit you over the head with a wrench. And it's really scary.

[Kevin Roos]

Yeah, it's really scary. And I should say this is something that I think crypto people have been right about. Years ago, when I was covering crypto more intently, I remember people sort of telling me that they were hiring bodyguards and personal security guards. And it seemed a little excessive to me. These were not, by and large, famous people who would, like, get recognized on the street, but their whole reasoning process was that they were uniquely vulnerable because crypto is very hard to reverse. Once you've stolen it, it's very hard to get your money back from a criminal who steals it. And that meant that they were more paranoid than like a CEO of a public company would be maybe walking around.

[Kasey Noon]

You know, I read a blog post on Andreessen Horowitz's website recently. So, you know, I was having a great day. And they've hired a former Secret Service agent to, among other things, help crypto founders prevent themselves from getting hit over the head with a wrench. And he has sort of an elaborate guide to, like, the things that you could do. But my main takeaway from it is if you're a crypto millionaire, you have to spend the rest of your life in a state of mild to moderate anxiety about being attacked at any moment, particularly if you're out in public.

[Kevin Roos]

Yeah, I do think it justifies the sort of lay low strategy that a lot of crypto entrepreneurs had during the first big crypto boom, where they would sort of have these, like, anonymous accounts that were out there that were them, but no one really linked it to their real identity. I think we are going to start seeing more people, especially in crypto, using these sort of synonymous identities. I mean, this is one of the reasons that, you know, people say that Satoshi Nakamoto has never wanted to reveal or herself after all these years is because there would be a security risk associated with that. But I think this is really sad. And criminals cut it out.

[Kasey Noon]

And here's my message to all the criminals out there. I don't own any crypto and I will continue to not own any crypto. You can keep your wrenches to yourself.

[Kevin Roos]

All right, last up on the docket for today. This one. Oh, I love this one. Casey, I've been dying to talk about this one with you.

[Kasey Noon]

Tell me.

[Kevin Roos]

Elizabeth Holmes partner has a new blood testing startup. So, Casey, you may remember the tragic story of Elizabeth Holmes. Yes. Who is currently serving an 11 plus year prison sentence for fraud that she committed in connection with her blood diagnostic company, Theranos.

[Kasey Noon]

Because God forbid a woman have hobbies.

[Kevin Roos]

Well, Elizabeth Holmes has a partner named Billy Evans. They have two kids together. And Billy is out there raising money for a new startup called Hamanthis, which is. Drumroll, please. A blood diagnostics company that describes itself as a radically new approach to to health testing. This is according to a story in the New York Times by Rob Copeland, who says that Billy Evans Co. Is hoping to raise $50 million to build a prototype device that looks not all that dissimilar from the device that put Elizabeth Holmes in prison. The Theranos mini lab. And according to this story, the investor materials don't mention any connection between Billy Evans and Elizabeth Holmes.

[Kasey Noon]

Hmm. Well, I wonder why that is. I have to say, she does have some experience that is relevant here, Kevin. Why not lean on that? Now, do we know what Hamanthis means? Is that like, sort of a name taken from historical antiquity? And we'll look it up and it turns out it's like an ogre that used to, like, stab people with a spear or something.

[Kevin Roos]

I assumed it was, like, ancient Greek for like, we're serious this time.

[Kasey Noon]

It's. According to Wikipedia, Kevin. It's actually a genus of flowering plants that grows in Southern Africa, but members of the genus are known as the Blood Lily. And I want to say, is it too late to change the name of the company to Blood Lily?

[Kevin Roos]

Yeah, that one. I like that one better. I did spend some time this morning because I was on my commute just trying to brainstorm some better titles for this startup that is run by Elizabeth Holmes, partner and does something very similar to Theranos. All right, let me run these by you.

[Kasey Noon]

Okay.

[Kevin Roos]

Blood test two. Electric Boogaloo.

[Kasey Noon]

No Fake tricks.

[Kevin Roos]

Reloaded. That's a Matrix Reloaded.

[Kasey Noon]

I like that. It was high concept.

[Kevin Roos]

Okay, here's one.

[Kasey Noon]

Okay. Thera.

[Kevin Roos]

Yes.

[Kasey Noon]

That'S good. Let's go with that one. Okay, well, good luck to Billy Evans with Thera. Yes. $50 million. Andreessen Horowitz will give that to them. You know, they love to be controlled. Yeah, I think. I think. Here's what. Here's my prediction. This startup is going to get funded and they're going to. They're going to release something. Yeah. And you're going to have to figure out how to keep your family safe from it.

[Kevin Roos]

Listen, if they're doing another fire fest, they're going to do another Theranos. You better believe it. We have learned nothing.

[Kasey Noon]

Theranos is back.

[Kevin Roos]

Well, Casey, that brings to a conclusion. This week's installment of Hard Fork Crimes Division.

[Kasey Noon]

And all the criminals out there. Keep your nose clean, stay low, try to stay out of the funny pages.

[Kevin Roos]

You're on notice, Sam.

[Kasey Noon]

This podcast is supported by Sophos. Many businesses think they're not ready for enterprise level tech, or they're stretched too thin to manage that tech. But with Sophos, no matter your business size, you get enterprise grade technology and real world expertise. Always in sync, always in your corner.

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Than 500 analysts who are on hand to alert you when not if, a cyberattack attack strikes. And you can manage all security alerts effortlessly in Sophos Central. Don't sacrifice your peace of mind as you grow your business. Learn [e-mail] this episode is supported by Eleven Labs, the company behind AI voices.

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That don't sound like AI voices.

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Experience what truly human like AI voices can do for your applications. Visit ElevenLabs IO hardfork to get started for free.

[Kevin Roos]

Hard Fork is produced by Rachel Cohn and Whitney Jones. We're edited this week by Matt Collette. We're fact checked by Ana Alvarado. Today's show is engineered by Chris Wood. Original music by Diane Wong, Rowan Nimisto and Dan Powell. Our executive producer is Jen Poyant. Video production by Sawyer Roquet, Pat Gunther and Chris Schott. You can watch this full episode on [e-mail] hardfork Special thanks to Paula Schumann, Kui Wing Tam, Dahlia Haddad and Jeffrey Miranda. You can email us as [e-mail] Send us your ideas for a blood testing startup.

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