Anthropic's Labs Lead On Fable's Capabilities + Building AI-Native Products — With Mike Krieger

Summary

Big Technology Podcast – Episode Summary
Title: Anthropic's Labs Lead On Fable's Capabilities + Building AI-Native Products
Date: June 24, 2026
Host: Alex Kantrowitz
Guests: Mike Krieger (Lead, Anthropic Labs & Instagram Co-founder), Lauren Goode (Co-interviewer, Wired)

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Episode Overview

In this episode, host Alex Kantrowitz and co-interviewer Lauren Goode (Wired) speak with Mike Krieger, former Instagram co-founder and now Lead of Anthropic Labs. The discussion revolves around Anthropic’s internal "Labs" organization, the capabilities and sudden withdrawal of its advanced model "Fable," the interplay between AI research and product development, and the cultural and ethical considerations shaping the future of AI-native products. Mike gives rare insights into how Labs prototypes new products, handles regulatory tensions, and attempts to balance innovation, risk, and competition in the rapidly shifting landscape of generative AI.

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Key Discussion Points & Insights

1. Mike Krieger’s Role and Focus at Anthropic Labs

• Transition from CPO to Labs Lead:
  Mike Krieger described his transition from Anthropic's Chief Product Officer to Head of Labs about five months ago, noting reduced proximity to direct regulatory interactions (e.g., with the White House) but a continued product-driven focus.
  “Less than in my CPO role. So I transitioned about like five months ago into this labs role...as a product person I want to make sure that [access] gets restored but less close to it than in that sort of C level role that I had before.”
  – Mike Krieger [01:46]

2. Fable Model Withdrawal & Backlash

• Swift Government Reaction:
  The release (and rapid withdrawal) of Fable faced intense scrutiny due to perceived regulatory/safety concerns, notably regarding Anthropic’s relationships abroad.
  “The sort of reaction decision was surprising... we're dealing with unprecedented times... they can develop really quickly as well.”
  – Mike Krieger [04:11]

• User Demand & Community Reaction:
  Despite Fable’s very limited availability, community enthusiasm was enormous.
  “Every time I've tweeted since then, they've mostly been like, bring back Fable... It struck a nerve. Fable will come back before Gotham did...clearly the folks that have gotten to use it and started incorporating it, it's actually really interesting.”
  – Mike Krieger [02:23]

• Why Single Out Fable? Safety Uplift:
  Mike attributes attention to Fable not to unique risks, but to its magnitude of “capability uplift.”
  “Uplift is a thing that we think about a lot when we think about model safety...comparing the uplift from a layperson using the model versus an expert or a layperson just using the Internet...”
  – Mike Krieger [05:14]

3. Material Impact of Fable’s Absence

• Labs Hamstrung by Ban:
  Fable’s withdrawal directly impacted Labs’ research and productivity.
  “Fable is the best model I've ever used...work has not stopped, but it's definitely, like, less good than the other models... Even in my personal use, I’m like, oh, I’m on Opus 48, and it’s good...but…it is noticeable, for sure.”
  – Mike Krieger [08:25]

• Difference with Super-Advanced Models:
  Fable and Mythos allowed for much higher autonomy and success in large, complex tasks.
  “The big shift…was going from delegating chunks... to delegating much more of a goal than just a [bug fix]. I moved much more to before going to bed, making sure I had queued up for Fable enough chunky work to last... I would check in later and it got it done in an hour...”
  – Mike Krieger [09:33]

4. Concrete Examples of AI-Native Product Work

• Delegation at Scale:
  Mike was able to delegate a massive language conversion from Python to Typescript (hundreds of thousands/millions of lines) – including planning, execution, and double-verification.
  “I basically…trusted it to not just do the individual action, but here’s a whole language conversion...plan it, execute, verify, double verify... I came back to the work being complete.”
  – Mike Krieger [11:45]

• Accuracy and Reliability Leap:
  “You're saying it was more correct, so it's faster, it's more accurate, more reliable. And then according to the US Administration, dangerous.”
  – Lauren Goode [13:05]

• Richer Project Understanding:
  “It has a greater...theory of project...The best engineers keep in mind all the disparate parts...and they also see around the corners...That’s been a significant difference I’ve seen in that class of models.”
  – Mike Krieger [13:17]

5. Why Anthropic Needs Labs: The Lab’s Mandate

• Purpose Circa 2024 vs. Now:
  Labs began as an experimental vanguard to keep Anthropic’s own products from “falling behind” its advancing models, bridging the gap between potential and reality.
  “A good litmus test for me is when we get ready to release a model, do we have a product or demo...very different... Labs at the time was, let’s make sure our products don’t fall behind the model exponential happening.”
  – Mike Krieger [14:06]

• Guiding Exercises:

  • Close the Product-Capability Gap:
    “Visualize the gap between what the models can do today and how most people use it. Can we close that gap?”
  • Anticipate Model Progress:
    “Imagine what the models are bad at now that they're actually going to be really good at in six months and let’s make sure we have a product ready for that...”

• Product Examples:

  • Claude Code: Originated in Labs as a “totally new form factor” for coding, responding to forthcoming model leaps.
  • Computer Use Automation: Initially nonviable, but served as an internal benchmark to track when capabilities grew sufficient to launch.

6. Anthropic’s ‘Frenemy’ Relationship with Build-on Partners

• Platform vs. Product Provider Dilemma:
  Startups question whether Anthropic will compete with them after partnerships; Mike’s take is that Labs should only do things that move the entire field forward. “If we’re ever entering an industry where all you’re doing is the same thing everybody else is doing...I feel like that’s a bad use of our time...If we're going in somewhere, it should hopefully be to...show the way where other products can incorporate them too.”
  – Mike Krieger [18:20]

• Dual Existence and Transparency:
  Drawing analogies to Amazon, Anthropic tries to balance being both a platform and product builder by being transparent, building shared building blocks, and not keeping the best tools internal only.

7. Culture, Values, and the Ethical Dimension

• Mission-driven Approach:
  Mike describes Anthropic’s internal culture as deeply committed to ethical AI, distinctly different from his Instagram experience, with social mission foregrounded constantly.
  “They walked the walk and really deeply believe in trying to make AI go well for humanity. That's like in the water internally...I remember at Instagram...we did a weekly all hands and talked about product 95% of the time...At Anthropic...it's a very mission driven AI company.”
  – Mike Krieger [23:03]

• Potential to Change the Valley’s Culture:
  “I think the conversation around how AI could or should go is one that is happening in real time...and I think that is a good thing.”
  – Mike Krieger [24:58]

• Philanthropy and Real-Time Reflection:
  “Positive signs...are a renewed interest in philanthropy...and that conversation around how AI should go is happening in real time with the technology.”

8. The Future Roadmap: Product Directions and Moonshots

• Agency and Self-Knowledge:
  Giving Claude or agents more understanding and agency within their environments to deliver richer, more autonomous help.
  “The first one is giving Claude an environment where it has more agency and...self knowledge...If you give Claude a lot of notion of its environment...it doesn’t go like, ‘I don’t know, I hit an issue’; it could [diagnose] for itself...It sounds kind of obvious, but it’s actually night and day in terms of how expressive these products are”
  – Mike Krieger [27:28]

• Closing the Workflow Gap:
  Making software that closes the distance between “how people understand their own work and how the actual day-to-day is to do that work,” automating tedious manual steps. “She’s like, ah, this is the first time in my career and she'd been working for 30 years...where what's in my head and what I am using is now this.”
  – Mike Krieger [30:09]

• Space-based Compute & Moonshots:
  While not Labs’ focus, Mike discussed the plausibility and growing seriousness of “data centers in space.”
  “I talked to somebody who is a person who sends things to space...they were really bullish...effectively infinite power and...infinite land...after that talk I was like, okay, I really can understand why this might make sense.”
  – Mike Krieger [31:48]

9. AI Model Economics: Tokens, Pricing, and Productivity

• Token Usage and Outcome-Based Pricing:
  While prolific token use gets attention, actual productivity doesn’t always correlate. Mike favors efficiency and outcomes.
  “There's not a lot of correlation between the person using the most tokens and the person that, I…think is most productive...Seems dangerous to...purely glorify the maximum usage. Obviously it's very gameable.”
  – Mike Krieger [33:14]

  “When I was still in the CPO seat I was thinking a lot about outcome-based pricing as something that would be really interesting to do if you could do it....But I would like to see some more experimentation around can you better capture what it's worth to the individual…”
  – Mike Krieger [34:30]

10. Startup Productivity, App Fatigue, and The Future of AI-Native Products

• Consumer AI Products Still Hard:
  Despite ease of creation, breaking through in consumer AI is harder than ever due to platform entrenchment and the “data gravity” of incumbents.
  “When we were releasing Instagram...the apps...wild west... two kind of random people released an app...got to number one...That is much harder now...People call it data gravity. Even if somebody has a 2x better AI doc editor, you're gonna move all your stuff? Maybe. Probably not.”
  – Mike Krieger [36:47]

• Smaller Teams, Larger Impacts:
  With today's AI tools, Instagram could’ve launched with 4–6 people, not 13.
  “I think we could have gotten there with like four to six...Or...do things in more than a single track...We could have done [Android] probably in a week with the models.”
  – Mike Krieger [38:45]

11. Product Safety, Social Harms, and Frontloaded Ethics

• Labs’ Approach to Product Harms:
  “There are certainly products that we have...conceptualized and been like, this product...if shipped would be bad for the world...I think asking that question a lot internally makes a difference. And...it’s a luxury to have core products and models that are doing really, really well...”
  – Mike Krieger [41:07]
• Baking in impact thinking and real-world economics/ethics from the start is now common at Anthropic.

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Notable Quotes & Timestamps

“It struck a nerve. Fable will come back before Gotham did… You don’t really know until you’ve put it through its paces… I almost just completely block out the noise in the first couple days of any new model release.”
— Mike Krieger [02:23]

“The best engineers keep in mind all the disparate parts of how this thing’s— and they also see around the corners… That's been a significant difference with that class of models.”
— Mike Krieger [13:17]

“I think if we're ever entering an industry where all you’re doing is the same thing everybody else is doing, but you’ve got the Anthropic brand— I feel like that’s a bad use of our time.”
— Mike Krieger [18:20]

“They really deeply believe in trying to make AI go well for humanity. That’s like in the water internally...”
— Mike Krieger [23:03]

“The first time in my career—she’d been working 30 years—where what’s in my head and what I am using is now this, it is now closed. I want to bring that feeling to everybody.”
— Mike Krieger [30:09]

“There’s not a lot of correlation between the person using the most tokens and the person...most productive...It seemed dangerous to...purely glorify maximum usage.”
— Mike Krieger [33:14]

“We could have gotten there with, like, four to six [people].”
— Mike Krieger [38:53]

“There are certainly products...we have...conceptualized and been like, this product...if shipped would be bad for the world...”
— Mike Krieger [41:07]

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Engaging Moments & Humor

• Fable/Gotham Filter Parallel: Krieger likens Fable users’ desperate craving for re-release to Instagram users’ perennial pleas for the (infamous) “Gotham” photo filter. [02:23]
• Interns as an AI Metaphor: Fun banter around the (sometimes offensive) meme of LLMs as “managing interns” [12:58].
• Lauren Goode’s “move fast and jailbreak things” Motto: [04:50] (play on Facebook’s classic wall poster).

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Structure Guide with Select Timestamps

• 01:34 – Mike Krieger’s role at Labs and recent transition
• 02:08 – Fable user backlash; Gotham filter analogy
• 03:41 – White House / regulatory response, SK Telecom issue
• 05:14 – Why Fable was singled out: “capability uplift”
• 08:25 – Fable’s withdrawal affecting Lab’s productivity
• 09:33 – What using super-advanced models (Fable/Mythos) unlocks
• 11:45 – Specific “million-token” Python to Typescript migration
• 13:17 – Models’ project-level thinking vs. shallow delegation
• 14:06 – Rationale and core workflows within Labs
• 17:44 – Platform-product tension: Labs vs. the build-on ecosystem
• 23:03 – Culture and values, how Anthropic differs from Instagram
• 27:28 – Product direction, “agency and self-knowledge” in Claude
• 31:48 – Data centers in space: why the concept is gaining traction
• 33:14 – Token use, pricing, and measuring output
• 36:47 – App fatigue, sticky incumbents, and the gravity of user data
• 38:53 – Instagram with 4–6 people? AI’s team-size revolution
• 41:07 – “Frontloading” product safety and ethical screens

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Key Takeaways

• Advanced AI models are dramatically changing how products are built—allowing smaller teams, higher autonomy, and entirely new form factors for user interaction.
• Anthropic Labs exists to bridge the capability gap, building ahead of the curve and serving as an internal vanguard to anticipate and productize future advances in AI.
• Amid hype cycles and regulatory scrutiny, Anthropic maintains a strong internal culture focused on mission, ethics, and transparency, even as scale and valuation rise.
• Product safety, as well as startup-versus-platform dilemmas, are being tackled head-on, with real-time transparent communication with build-on partners.
• AI’s full consumer breakout remains elusive, due to market consolidation and the gravity of incumbent data/services, but internal advances continue at an exponential pace.

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For Listeners:

Whether you’re a founder, builder, investor, or just following the next big thing in tech, this episode offers a rare look into the intersecting worlds of AI R&D, product innovation, and the values—and tradeoffs—powering one of Silicon Valley’s most influential new organizations.

Transcript

[Alex Cantrowicz]

Anthropic is not just a massive model builder, it's a massive product builder as well with products like Cloud Code and Cowork that have taken off like crazy over the past few months. And Claude Code came out of a place that few of us know much about called Anthropic Labs. And Anthropic Labs is a organization within Anthropic that is working on building the next level of frontier products with AI at the center. And so today we are lucky to hear from the person running that lab, Mike Krieger, who is the co founder of Instagram and now the lead of Anthropic Labs. At Anthropic. We're going to welcome him on stage along with Lauren Goode of Wired who will join me as a co interview. Mike and Lauren, let's hear it from both of you guys. You're here. In the face of ongoing disruption and opportunity, TMT leaders need to deliver tangible results and not just ideas when pace and performance matter most. PwC combines market insights and deep sector experience with AI, cloud and emerging tech to accelerate your transformation and drive measurable ROI. From strategy to execution, PwC can help you anticipate what's next, outpace disruption and compete. For more information, visit pwc.com

[Mike Krieger]

all right.

[Alex Cantrowicz]

All right, Mike. So chill times in Anthropic land.

[Mike Krieger]

Nothing going on.

[Lauren Goode]

Slow week, Slow week.

[Alex Cantrowicz]

You want to start, Lauren?

[Lauren Goode]

Yeah. First of all we want to talk about what you're working on at Labs and explain your role to folks. But I want to ask you first, how close are you right now to the situation with the White House?

[Mike Krieger]

Less than in my CPO role. So I transitioned about like five months ago into this labs role. I think in the CPR role I think would have been deep in it. Now I'm more. Obviously we want to restore it and as a product person I want to make sure that that gets access but less close to it than in that sort of C level role that I had before.

[Lauren Goode]

Okay. Alex, do you have a follow up?

[Alex Cantrowicz]

I have like eight follow ups, definitely. Well, there was a guy named Ben on X who said I will mail Anthropic an original copy of my long form birth certificate if they will enable Fable for me again. I sound like those lunatics who are obsessed with four. Oh, now will you take Ben's long form?

[Mike Krieger]

I don't know that we'll take his long form, but it has been interesting. I mean Fable was only available for a few days, but I've definitely. Every time I've tweeted since Then they have not read whatever I was tweeting, and they've mostly been like, bring back Fable. Which, like, on Instagram, we got rid of Gotham. Do you remember Gotham? The filter? This is like. And then for the rest of, like, the next eight years, all I heard was bring back Gotham. So it struck a Nerf. Fable will come back before Gotham did. But, yeah, clearly the folks that have gotten to use it and started incorporating it, it's actually really interesting. I've learned to not really trust day of or even week of model reactions. You don't really know until you've put it through its paces. And so I almost just completely block out the noise in the first couple of days of any new model release because, I don't know, everybody has maybe their toy example thing that they like to do with a new model, but it's hard to actually put it through its paces until you've actually had real work done with it. And I think were just starting to do that. And then we had to sort of pull back Fable. But I remember in December when we put out Opus 4. 6, it was like this interesting time where everybody went home for the holidays, and a lot of people had that week off between Christmas and New Year's, and then they came back and were like, oh, I spent a lot of time, and I really get why Opus is good and I'm gonna do it. So I don't think Fable has had that opportunity yet.

[Lauren Goode]

But despite that, though, I mean, this was a pretty big reaction from the Trump administration. And I think everyone here, especially if you were listening to the Alex stuff demo session, understands what's going on. But this happened within a few days of the model release. If I can give Wired some credit, Wired just reported last night that it was due to Anthropic's relationship with or having given access to the model to SK Telecom. That could have raised flags within the administration. How surprised were you by how immediate that backlash was?

[Mike Krieger]

Yeah, I think the sort of reaction decision was surprising, and we were sort of immediately engaged with them to restore access as well. And so at the same time, one thing that we think a lot about internally is there used to be a poster on the Facebook wall when we were still. That was like, every day feels like a week. And I think that's becoming true in AI. And I think a good thing to remind ourselves of in general in the industry is we're dealing with unprecedented times, we're dealing with new situations, and they can develop really quickly as well. And so I think also developing the capabilities and the connections and to make sure those conversations can happen quickly is really, really important.

[Lauren Goode]

I have a new motto suggestion for you for the wall, move fast and jailbreak things.

[Alex Cantrowicz]

I don't think they're going to use that, Lauren.

[Lauren Goode]

Okay,

[Alex Cantrowicz]

we just heard from Alex that some of these capabilities have been available on previous models. You can find bugs with other with other models that are available today. So why do you think Anthropic got singled out on this front?

[Mike Krieger]

I don't know why Fable specifically was singled out as again, Fable being the non cyber intention model as well. I think the thing that does change over time is there's the capabilities, if you knew what to prompt and knew what you were looking for, and then there's the capabilities. I think. I'm not sure I resonate with the juicing high school athletes metaphor from Alex, but the uplift that you get. Uplift is a thing that we think about a lot when we think about model safety. So if you look at our model cards, for example, one of the ways we look at risk in the bio domain is comparing the uplift from a layperson using the model versus an expert or a layperson just using the Internet and seeing what the comparison there is. And so that is one trend that has been progressing as the models get more capable. And so maybe less why Fable is singled out and maybe more like what the overall trajectory is.

[Alex Cantrowicz]

Interesting. So we just had Ara Kharazian, the lead economist on Ramp, on the podcast, and one of the interesting things that he said was during the Pentagon situation, there were these headlines, okay, this company will not use Anthropic anymore. But actually the data from Ramp shows that spending actually increased to Anthropic models. It was apparently a good publicity moment for the company. So I think it sort of plays into a debate that we have here on the show about how much of this is like real concern for the issues and how much of it is from Anthropic is marketing. And like we have somebody from Anthropic here who can actually shed some light on that. We just said Stamos talking a little bit about it from his perspective on the security side. But we're lucky to have you here today. So is it material or is it marketing or some combination?

[Mike Krieger]

I mean, I think it is one of the hardest things to like really deeply believe that something is true and not marketing. And then due to not even just Anthropic, I think like, you know, I think people are generally right to be skeptical of any company saying anything. And you should, like, put it through your own filters as well. But for me personally, I'm always like, no, but it's real. And, like, we both deeply care about safety and are like, to the extent that we are being vocal about anything, it is to either sort of help paint the picture of what very likely is coming or we believe is coming, or what we've already seen and spotted, for example, in the Mythos case, really just looking at, like, vulnerability scanning and bug finding and doing it in partnership with companies that were in that kind of Project Glasswing initial announcement. And so the technology is awesome. Not in the. Like in the. Just it is doing really incredible things. And therefore even calling out what we see as what is happening, I think can seem hypey. I wish I could press a button and I could make everybody believe that we are not being hypey. I realize that's not the reality that we operate in, but at least from my perspective, we try to call it like it is.

[Lauren Goode]

My understanding is that within labs, in particular in research and development at Anthropic, that you're using the best AI models to actually build new products, to prototype new products, and sort of test out your thesis. So is this ban essentially now limiting your ability to do that within labs?

[Mike Krieger]

Yeah, I mean, definitely. Fable is the best model I've ever used. And it's not to say that work has stopped, but it's definitely, like, less good than the other models that. Or, sorry, we are using models that are less good than that as well. And it is also, I mean, maybe the reverse of the distrusting the first week response is what happens when you don't have Fable. And obviously the Twitter reaction for the people that had already kind of gotten into the model and were using it was strong. But I'd say even in my personal use, I'm like, oh, I'm on Opus 48, and it's good. I'm still productive. I'm doing work. And we can go into how my work changes changed with these Fable or the models of that family. But it is noticeable, for sure.

[Alex Cantrowicz]

Yeah, I think we'd like to know that. I mean, the public had access to Fable for like a half a minute. Groups in this, like, Project glasswing have had access to Mythos. We don't really know what the difference is between using a model that you can use today is and using one of these super anthropic models. So what actually could you do differently with a Fable or a Mythos?

[Mike Krieger]

I think for me, it's the sort of scope and Scale of delegation and all these things are really imperfect. People say, like, oh, is this now a level five software engineer or level six? But anybody who's used these models extensively knows that they're still spiky in capabilities, right? In some ways, in many ways they're a better engineer than me. And in other ways, like I was complaining today that it had missed a descender, like the G, that bottom part is called the descender. I'm like, how did you put that in the ui? And it's clipped. And of course there's vision capabilities that need to improve and there's debugging capabilities and there's sometimes even just sort of human common sense that is more way better at than the models are. But overall, I think the big shift for me working and it was really interesting because it sort of coincided with me going back into a builder role. So I really got to see going from using these models as an executive, you're trying to do the most of them but not going to have it write all your email and I think strategy still needs to come from you and then you can use the models to pressure test it. But going back into a builder role and going from, okay, I am delegating chunks like, please fix this bug, or I'm thinking of implementing this feature. Let's go back and forth to something that ends up being much more sort of, all right, I got this bug report from one of our users, or I have this notion of something that I want to build. Can you sketch out two or three ways in which we could do it? That seems plausible. Often I find actually sometimes it'll give me the explanation or proposal and be like, ok, that actually is over my head. You are clearly way smarter than me. Explain it to me like I'm maybe not 5, but at least not you. And it'll sometimes explain it that way, but then go build it and getting it right at a very, very, very high rate. And I think that starts really changing how you operate. I moved much more to before going to bed, making sure that I had queued up for fable enough chunky work to last. I would call it the whole night and I would check in later and it got it done in an hour and it was like, I guess, hanging out for the next seven hours, but really delegating. Much more of a goal than just a.

[Alex Cantrowicz]

So one task, for example, like give us an example of one task you would hand to it.

[Mike Krieger]

I mean, here's a kind of crazy one which is for the programmers in the audience. I had Written one of our labs projects in Python. That's like the language I know. All Instagram was all Python and for some not super exciting reasons, we actually needed it to be in Typescript to deploy it. And I was like, all right, that's going to be in Instagram. We for years talked about moving from Python to PHP or Hack or the Facebook language after the acquisition. And at least when I was there, I never did. But I basically, we have a feature called Dynamic Workflows where you can have it also break down the task into a lot of subtasks. I trusted it to not just do the individual action, but here's a whole language conversion of millions, hundreds of thousands of lines of code at that point, go off and do it, go plan it, go execute it, go verify the work, double verify the work. And then I came back to the work being complete. So that level of this is a big, chunky task.

[Lauren Goode]

So that was. So you're basically saying it was faster, did it in an hour. You're guessing with Fable compared to. What would it have been before?

[Mike Krieger]

I think the main difference is in the past it would be like, great, I did it. And you'd be like, did you. You kind of took a shortcut here, or this is not quite right, or I need to go verify it. Or like, oh, you cut this corner.

[Lauren Goode]

Or it's like the managing interns thing that everyone's been saying for the past year, which is very offensive to interns, by the way. But yes, yeah, exactly, exactly.

[Alex Cantrowicz]

I don't know. Have you managed an intern?

[Lauren Goode]

Yeah, that's true. And you're saying it was more correct, so it's faster, it's more accurate, more reliable. And then according to the US Administration, dangerous.

[Mike Krieger]

I think the other piece is it has a greater theory of mind is the wrong word, but theory of project, so that it's less, oh, I'm going to make this change. And it'll say, great, I'll make this change, but really, especially if you've done software engineering at scale, the best engineers keep in mind all the disparate parts of how this thing's. And they also see around the corners, I can make this change, but if I don't do it in this way, then the next change is going to be incrementally harder. I think that's been a significant difference I've seen in that class of models.

[Lauren Goode]

So I think when we talk about Anthropic labs, people think of Claude code because it is really your breakout product. And it sounds like you've Been tasked with basically figuring out what the next Claude code is. Would you say that's an accurate description of what you're doing at Labs? And also why does Anthropic need labs?

[Mike Krieger]

Yeah, it's also maybe worth thinking about why we needed labs in 2024 when I arrived and why we need labs today. Because I think that the answer kind of shifts. I started the labs, the original Labs team, with Ben Mann, who's one of the co founders of Anthropic, in my third week at Anthropic. And it had been something that had been bubbling under. And at the time, the reason was really different. It was all of our product engineering team was 25 people and we didn't have the models really. We had Claude when I joined. It was sonnet, like an Opus 3. Like those are for the time, good models, but you weren't going to. They were not even interns. Right. They weren't even IC3 engineers. So if you have a team of only 25 or 30 engineers, they are working on like the next incremental thing. And we were feeling like the models are starting to get better, but we don't have any products that sort of show that off. Like a good litmus test for me is when we get ready to release a model, do we have either a product or a demo or some other illustration of something that is very different and it gets harder over time, like with. With Fable even illustrating that weekend task or this longer amount of work. So really, Labs at the time was, let's make sure we don't. Our products don't fall behind the model exponential that's happening. And yeah, so Claude code came out of that initial one because nobody in the rest of the product orgs people were thinking about coding, but nobody had the space to go and think about, well, what if we totally changed the form factor and we embraced the fact that the models were going to evolve in this way. And a lot of the two most useful thought exercises we do in labs. One is visualize the gap between what the models can do today and how most people use it. And can we close that gap? That's one. And the other one is imagine what the models are bad at now that they're actually going to be really good at in six months and let's make sure we have a product ready for that by then. I think those are the two guiding questions for Labs Then also out of that first incarnation came computer use. Computer us was different though, because when we built it, it was really bad. We tried a bunch of products with it. And this was around Sonnet 3.5. And be like, claude, can you help me clean up my desktop? And it would click the thing, delete the file. This is not safe for release. We're definitely not going to go and build this or to ship this. But we had that product so that every new model that we'd release, we'd first check it internally and say, did computer use get better? And we told the research team how it had gotten better or worse until the moment where we said, it's good enough. We're actually going to put a product out around this. It also gives you this beacon into the future that you can measure your future products against, but then compare it to now. So we have a thriving product team. There's cowork, there's Claude. Code has grown a lot. We have our platform, and now I think it's actually much less about none of these product teams are doing this sort of thinking. And I think it's much more that the models are advancing really quickly, and even our capability to interact with them needs to evolve. So one of the things we collaborated with labs and cloud code that we ship today is cloud code artifacts. Having cloud code not just be able to type back to you, but also sort of draw a picture or give you an illustration. And that partially came from spending a lot of time in lab saying, just a text box and a big text response is not going to cut it anymore. Like when I mentioned that the models feel like they're way smarter than me when they talk to me sometimes I'm like, can you draw me a picture? Because this is what I actually need to fully understand this. But it's really what we've been thinking about is, yes, we have a lot more products. We actually have a lot of consolidation to do in our products. That's another initiative that we have. But within that, we still have an opportunity to make things much more accessible to a person that does not spend all of their time thinking about prompting and the exponential and the difference between high, low and medium effort. There's a lot we can still do there.

[Alex Cantrowicz]

But Mike, so there's. It puts people using anthropic models in an interesting place, right? Cursor, I think, just sold for 60 billion to SpaceX. And someone put this meme on Twitter. That cursor would have sold for 300 billion if it wasn't for this guy. It's a picture of Boris Cherny, the person who created Claude Code for companies that are going to build on top of Anthropic technology. They're going to wonder, do I want to partner with Anthropic or is Anthropic going to go ahead and build the product that I'm going to want to build potentially even after partnering with them?

[Mike Krieger]

Yeah, I mean, we'll take the agentic coding side and I think the broader sort of aspect of being both a platform and a product I think is really interesting. When we take on projects, the goal is often to sort of push that area of the industry forward. So there were AI coding editors and some of them were really good, but nobody was quite thinking about it in as sort of free form a way as we got to think about it with Claude code. And now a lot more products have that flavor than I think would have otherwise. And so I think if we're ever, you can call me out on this, Alex. If we're ever entering an industry where all you're doing is the same thing everybody else is doing, but you've got the anthropic brand, I feel like that's a bad use of our time and a bad use of our either labs or product team time. If we're going in somewhere, it should hopefully be to say, all right, we think that the direction of travel is this way we can build a product of that. And then by the way, there's no world, nor should there be a world where all the products are anthropic products. That would be a bad world. So that is hopefully either creating new space for companies or showing the way where other products can incorporate them too.

[Lauren Goode]

It would almost be like working for a tech company that has social messaging video. Right, Mike? Yeah. Okay. Well, there was some question, for example, when Anthropic launched a product that was seen as competitive to figma. And you had been on the figma board prior to that and I think you stepped down.

[Mike Krieger]

Is that correct?

[Lauren Goode]

Yeah. And so it's a good question that Alex has brought up, I think where Silicon Valley is known for this really healthy, vibrant, risk tolerant startup ecosystem. And when the big start coming in with tons of venture capital and a lot of resources, people say, well, wait, are they just, are they essentially just going to steal my idea?

[Mike Krieger]

Yeah, no. I think our dual existence and it's something that other companies have to navigate. We talked about Amazon a lot in the previous panel. They have to navigate this role where they are both infrastructure provider. They obviously have a very large E commerce. They do video, but they also serve video. And then by and large customers can live in that dual world of like okay. I'm using their infrastructure also, knowing that they are also using their infrastructure to do that. And I think you can talk to our customers and see how well we're doing at this. The thing I always try to do is at least approach it with a lot of transparency. So the cursor example is an interesting one where Michael and I talked a lot over the time around. Here's where things are heading and similarly with the other products that we think about, I think it's a couple of things. It's transparency and then it's shared building blocks. I think in general, and I actually don't think there's any cases where this is even true. We're trying to build on top of the same capabilities that are available elsewhere. The last time I was here in the Commonwealth Club on this stage was our healthcare day at the beginning of the year and we didn't ship Claude Healthcare. Only we have it, nobody else has it. We shipped a bunch of plug ins and skills and MCPs and complementary abilities. So that's how. I'm not claiming it's easy or that it's a straightforward thing, but it is how we're trying to navigate what is admittedly a complicated situation.

[Lauren Goode]

Speaking of startups, Anthropic is still technically a startup, but you're worth a lot of money. I mean, what's the latest valuation? Is it 965, $965 billion or something like that?

[Alex Cantrowicz]

You sold Instagram for a billion, right?

[Mike Krieger]

Startup in 2010.

[Lauren Goode]

Money financials have changed quite a bit since then. And yet Anthropic has positioned itself. It is a pbc and it's positioned itself as sort of a more ethical company around building AI. And I'm wondering if you could talk a little bit about how you see that positioning in Anthropic's role in particular, changing the culture of the Valley. I think back to how Google in the beginning of the 2000s really changed the culture of Silicon Valley in so many ways. And how do you see Anthropic's culture now dictating this next era?

[Mike Krieger]

Yeah, that's a really interesting question. Maybe I'll start Insight. And I think there's an external component too. I think the reason I joined in the first place. So I was winding down my second startup and knew I wanted to go work at a Frontier lab because I had started to use these models for coding and they were bad at coding, but I could see that they were as bad as they were ever going to be at coding. They were going to Improve. And I had started building on top of these APIs. So the startup I was doing was called Artifact and we did sort of AI powered, sort of news recommendations and actually read a lot of big technology via Artifact back in the day.

[Alex Cantrowicz]

Thank you.

[Mike Krieger]

Things we added, so.

[Lauren Goode]

But not Wired.

[Mike Krieger]

You know, you guys had a really hard paywall, to be honest.

[Lauren Goode]

Fair enough.

[Mike Krieger]

We didn't do very.

[Lauren Goode]

Do you need a discount on my subscription so I can get one for you? Okay.

[Mike Krieger]

It's actually really funny.

[Alex Cantrowicz]

Like the bacon deals.

[Mike Krieger]

Yeah, making deals. It's the login cookies. It's like really hard to keep people ignorant.

[Lauren Goode]

I know, I know. Please escalate this to Conde Nast. I know.

[Mike Krieger]

And email logins. Very hard to do in a night. But I was building on top of the APIs and be like, wow, okay. They're able to do really interesting things. But ultimately made me go to Anthropic was like they walked the walk and they really deeply believe in trying to make AI go well for humanity. And that's like in the water internally. And I think has been why I think the company has remained as cohesive as it has even as we've grown. And I think that it's like a testament also to the co founders there on how often they are talking about this as well. It was a surprise for me coming from a world where at Instagram we did a weekly all hands and we Talked about product 95% of the time and maybe 5% of the time we talked about something else that was going on in the world around the company, probably maybe underselling our go to market. Maybe it was like 80, 20. But it was definitely a very, very heavy product. And I remember Anthropic about six months in myself and Kate Jensen, who's one of the leaders in the sales organization, did a joint all hands where we talked about our, you know, how we're doing product and go to market together. And people were like, this is so great. I finally understand our product strategy and like what we've been doing. I was like, oh, right. This is not quote unquote, a product company. You know, it is a very mission driven AI company with like a very strong sense of like why it exists in the world. I think in terms of the overall impact on the Valley remains to be seen. I think positive signs that I've seen are interesting signs of those. Things that I've seen are a renewed interest in philanthropy across the board. And I think that's something that has been written about and I think it will be an interesting sort of outflow. Again, who knows how all of this goes, but depending on how it goes, it could mean a lot of interesting new sort of philanthropic deployment. And then I think the other piece is the conversation around how AI could or should go is one that is happening in real time with the technology versus retrospectively, which I think has been the case for other technology waves. And I think that is a good thing.

[Alex Cantrowicz]

Hi everyone, Alex Cantrowicz here. I want to tell you about a documentary I've made with Gravity to explore the future of AI agent security. To find out if we're truly ready for autonomous agents. I sat down with MIT Professor Ramesh Raskar, former White House CIO Theresa Payton, Michelin's group chief data and AI Officer Ambika Rajagopal, and Sharon Guy, a former executive at Alibaba. They each offer unique insights into this evolving landscape. We conclude with Rory Blundell, CEO of Gravity, to discuss the path forward, with Gravity leading the way. Join us on this journey. You can watch the full documentary at the link in the show Notes.

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[Alex Cantrowicz]

Jobs no one goes to Hank's for spreadsheets. They go for a darn good pizza. Lately, though, the shop's been quiet, so Hank decides to bring back the $1 slice. He asks copilot in Microsoft Excel to look at his sales and costs and help him see if he can afford it. Copilot shows Hank where the money's going and which little extras make the dollar slice work. Now Hank says a line out the door.

[Mike Krieger]

Hank makes the pizza.

[Alex Cantrowicz]

Copilot handles the spreadsheets. Learn [e-mail] Work Mike, you talked a little bit about Anthropic has this gap that it sees between the capabilities of the models and where everybody is building products and with labs. What you try to do is get ahead of that so you can show people what AI might be able to do now and six months from now. So please tell us, please tell us what you're building, where you see the potential and what people should be on the Lookout for.

[Mike Krieger]

Yeah, it's a roadmap.

[Lauren Goode]

Oh, and if I can throw an. And what you're building now. But also if you have a pie in the sky, like Elon Musk, data centers in space type ambition. I want to hear about that too. Tell us everything.

[Mike Krieger]

Great.

[Alex Cantrowicz]

We have 13 minutes left.

[Mike Krieger]

Go exactly. The rest 30 minutes is the monologue in my product. I think maybe two themes. I'm really excited about that. We've been exploring a lot. The first one is giving Claude an environment where it has more agency and it also has more self knowledge. And I'm going to unpack that because that's like a lot of AIE words. But I'll give you an example of where we are currently doing a bad job of this. If you are in a Claude project and you make a file with Claude, you're like, that's great. Can you add it to our project? Claude will be, no, you have to go download the file and go drag and drop into this thing. And you're like, what? Until yesterday I would have said the same thing about Claude design and cloud code, where if you're in cloud code, you're like, cool, I need a design for this thing that we're building. Or you're in cloud design and you make a mockup and you want to go build it. Be like, cool, here's a zip file. And you're like, what? So that's a little bit of interoperability. But in general, this theme of if you give Claude a lot of notion of its environment, I was talking to actually a customer, like an API customer, and one of the things that they were experimenting with is actually even giving Claude a secure version of their source code while it's running in the agent loop in their product. So that if it hits an issue, it doesn't go like, I don't know, I hit an issue. It could be like, well, it's probably this thing, at least when it's talking to one of the maintainers of the software. And so that overall theme and of course you have to do it with safeguards and be really careful about what you unlock with it. It sounds kind of obvious, but it's actually night and day in terms of how expressive these products are end up being able to be. Right. And you can even see it going from maybe like core chat or classic chat in cloud AI and something like cowork, where it's got a little bit more agency and it has a runtime and it's able to sort of understand a little bit about its environment. But I think we are at like 10% of the journey about where we could go. Actually one of the reasons I think people got excited about things like OpenClaw is seeing how a harness that is modifiable and you can talk to it about things and you don't ever get the sense of like oh sorry, I can't do that. You're going to have to go to this like setting screen and turn it on. It's just a thing it has access to and hopefully with the right gardening and permission. So that's like theme one that I'm extremely excited about and I think if we do it right, should actually transform all of our products from head to toe. The other piece is and I'll maybe share not the internal product we're working on, but the phrase I got as feedback was I think closing the gap. I talked about closing the gap between capabilities and reality. I think it's also closing the gap between how people understand their own work and then how the actual day to day is to do that work. I was talking to somebody internally who's on our privacy team and to move a ticket from one queue through another one via the task tracker into another one was eight different steps of copying and pasting of manually moving. Pretty annoying to have to do, probably error prone, you have to keep spot checking it. And we helped her with one of our labs projects to basically make that not a pain. And she's like ah, this is the first time in my career and she'd been working for 30 years. We're like what's in my head and what I am using is now this, it is now closed. And I want to bring that feeling to everybody who like of course Cloud unlocked a lot of non technical people being able to code but we're still asking people to understand way too many concepts of what is the difference between my sandbox environment and production or connected MCP as myself or others or how should I store data? And of course you can't abstract everything but if you combine both of those themes, if you give Cloud a lot of self knowledge and you're creating an environment where it can actually solve complex problems for people in repeatable ways, I think I get very, very excited about those things.

[Lauren Goode]

And your moonshot not letting you off the hook. What's your moonshot?

[Mike Krieger]

Moonshot?

[Lauren Goode]

Yeah.

[Mike Krieger]

Nothing in space. Although I guess we're, you know, we're talking to SpaceX about spacey things but

[Alex Cantrowicz]

you're talking to SpaceX.

[Mike Krieger]

I mean those are the announcement for Compute.

[Lauren Goode]

Yeah. It was exploring extra. Extra orbital. What was the phrase? Something about exploring like post orbital world things.

[Mike Krieger]

Definitely not my department, but yeah, there's stuff in.

[Lauren Goode]

So the labs isn't working specifically with the team on compute.

[Mike Krieger]

Right? Exactly.

[Lauren Goode]

Or chips.

[Mike Krieger]

Separate. Separate.

[Lauren Goode]

Totally separate. Okay. Okay. So you're moonshot.

[Mike Krieger]

Yeah.

[Alex Cantrowicz]

Do you personally believe in data centers in space?

[Mike Krieger]

I had a conversation. I by far from a data center expert, but I talked to somebody who is a person who sends things to space, who is not Elon Musk.

[Alex Cantrowicz]

That's what you would say.

[Mike Krieger]

Yeah. And they were really bullish and I was trying to talk about why. And it was basically like effectively infinite power if you convert it well and you know, infinite land. And I was like, okay, you can buy that.

[Alex Cantrowicz]

I mean.

[Mike Krieger]

And I think they feel good about the shielding you have to do. Again, clearly not my area of expertise. But after that talk I was like, okay, I see it, you know, even if it's going to take a few years. At first I admittedly thought it was a crazy idea in general, but now I'm like, oh, I actually really can understand why this might make sense.

[Lauren Goode]

When you were talking earlier about the ways that the work in Claude is going to get compressed and all those steps, I couldn't help but think of tokens and how, you know, maybe it's good for your business model in the short term if people have to take so many steps and use so many tokens. But tokens have become this unit of economics that we're using to describe the industry now and people are token maxing and now they're tokenizing. And one, I want to see, I want to hear where you sit on that spectrum if you're a token maxer. And two, is there a near future in which the industry is not actually measured by tokens? It goes the way of MIPS or dial up or some other. There's some other unit of measurement that actually defines the economics of this era.

[Mike Krieger]

Yeah, I think both of those are really interesting questions. It was interesting earlier this year when you started hearing about companies that have dashboards showing who used it the most. And we of course have internal metrics as well. And we found that there's not a lot of correlation between the person who's using the most tokens and the person that I. It's an interesting thought. I do at your company is write down your 10 most productive questions, people that you think are most productive, and then get your top 10 token users and see how closely they correlate. At least for us, it wasn't that correlated? It seemed dangerous to sort of like sort of purely glorify the maximum usage. Obviously it's very gameable. But even beyond that I think it's yes, you can ask Claude to do 10 different variants on something, but if you thought about it deeply, maybe you would choose two that you thought were most promising and the third one if you then had some iteration on that as well. So I would not say like a token max. Actually the tokeniest thing was that conversion thing I did, which is like a couple million tokens. There's like a lot of tokens that it took to convert the thing from Python to Typescript. But I think people are being more thoughtful about these different pieces. And one of the things we look at whenever we look at a model launch is not just model intelligence, but we're also really thinking about model intelligence and effort and token efficiency as that combination. And I think that's a big lever we have to improve is how do we continue to be more and more token efficient for a given task so that you can also, hopefully you don't have to think very hard about this. We can do this automatically, but we're able to tune the solution to the problem a little bit more. And then to your second question. Yeah, when I was still in the CPO seat I was thinking a lot about outcome based pricing as something that would be really interesting to do if you could do it. And of course if you talk to the Sierras and fins of the world that have a really clear, we kept this, we were able to solve this customer request and not have it go escalated, that's really clear. It gets so much fuzzier on these tasks that we actually ask Claude these days. I had a strategy document, I used Claude to critique my strategy document. What was the outcome? It's like, well, I don't know, tell me how the strategy goes six months from now. It feels like it's going to be very hard to capture that as well. But I would like to see some more experimentation around can you better capture what it's worth to the individual and then whether the company and then can we find the best way to do that as well? And I guess the most concrete thing we've moved towards that and we have a product called Cloud Managed Agents where we'll run all of the infrastructure for you in terms of doing all of the agentic harness and calling the tools, et cetera and you can either do it in the normal mode, which is you give it tasks, it will go through tokens, it'll tell you when it's done. Or we have an outcome based mode where you can say, here's what good looks like, here's a rubric, go and do it and it'll go off and make it more outcome. So if everybody had moved on to that API, then I think maybe we could have a different outcome based pricing. But we'll see how that gets adopted.

[Alex Cantrowicz]

John or guys in the back. Do we have the random image? Can we show the random image? If we can. Great.

[Mike Krieger]

Excited. The random image.

[Alex Cantrowicz]

Oh, here it is. Okay. It's just because we didn't have a good label for it, so we just called it the random image because it might come up at any point. But this is a chart from the Financial Times, speaking of utility, where it shows the amount of app releases that have come out which are skyrocketing and then apps with significant usage that seem to be going down and app reviews which seem to be going down. So Mike, I'd love to hear you respond to what we're seeing in the image here. Is it possible that everybody's coding and releasing, but we're not really seeing a big boom in productivity?

[Mike Krieger]

That's really. I mean, I think there's definitely a power law in app US usage in general. It'd be interesting to seeing if any of those app release became one of the apps with significant usage.

[Alex Cantrowicz]

All right, we could take it down.

[Mike Krieger]

Yep, go ahead. I think this ties into something I've been thinking a lot about, which obviously my background is in consumer and I've been wondering what the consumer AI breakouts will end up being. And I don't know, we've seen a lot of them yet. And I think part of it is, you know, I don't know how far back that chart goes, but when we were releasing Instagram, it still felt a little bit wild west in terms of the apps. Like people were excited about apps and two kind of random people released an app and were able to get to number one in photos and video within three months. I think that is much harder now when you think about how consolidated the top 10 is and how much time spent is spent on the tiktoks and reels of the world. It's a lot. And so I think getting that breakthrough consumer experience I think is really, really hard. So I think that is as much a story about how sort of consolidated consumer products are these days, number one. Number two, how entrenched or how powerful it is to have that sort of data. People call it data gravity. Like the data gravity of something like your Google Docs are in your Google Docs. So even if somebody has a 2x better AI powered doc editor, you're going to move all your stuff? Maybe. Probably not. So I think that it speaks to the things that are sticky. I think about a lot the hard stuff is still hard. Making something people want still really hard. We have amazing models internally on top of not all of our products work. I think that's a bullish sign for product people like me because it means that I think we hopefully still add value. But I think that chart is maybe another place. It's harder in many ways than ever to break through, even if you can code more quickly. And could we have done Instagram in a month instead of three or four? Probably, but we got there after a long winding, turns and twists and turns process.

[Alex Cantrowicz]

You had, I think 18 people at Instagram when you sold it.

[Mike Krieger]

13.

[Alex Cantrowicz]

With these tools, do you think you would have how many people you think you would have had?

[Mike Krieger]

It's really interesting because of those 13.

[Alex Cantrowicz]

Just give us a number because everyone's like, oh, one billion dollar one person startup. How close could you guys have gotten?

[Mike Krieger]

I think we could have gotten there with like four to six, you know. Okay, yeah. Or the thing that we would have done if we'd grown it, we'd be able to do things in more than a single track. Like Instagram was. Have you ever watched my five year old plays soccer now? By which I mean like the ball is there and every single person runs to the ball. Like that was our product team. It was like video go and everybody like goes and works on the one thing and like we'd be able to like play positions like Android. We built in about a month for Instagram. We could have done it probably in a week with the models. And to build Android, we took everybody off iOS and we all relearned to code Android OS and then we went off and do that. And for that whole month we were barely shipping updates on iOS. I think you can be a lot more. Actually a really good example. There's a Labs project I have internally that helps accelerate how anthropic engineers code and do code review. And that that project I am maintaining an iOS and an Android version of and I basically have the cloud that works on the iOS one. Basically like ping the Android one and be like, hey, I implemented this. Sorry, Android users. It's still the second one even in a LLM world. Sorry. And then the Android origin is like, okay, I'm going to do this. Oh, that doesn't count because that feature doesn't make sense here. I'm going to drop it. And of course, we wouldn't have been able to delegate all of that on Instagram, but we sure could have done a lot by having platform parity. Like this dream of platform like close to parity is now actually quite doable.

[Lauren Goode]

You're probably going to get calls now from the remaining six or seven people on your Instagram team going, did I make the cut in the new era? Also, it sounds like you probably could bring Gotham back now if you really wanted to.

[Mike Krieger]

I forget if we eventually, I think for April Fools, maybe we brought it back one day.

[Lauren Goode]

Yeah, yeah. Do we have time for one more question?

[Alex Cantrowicz]

Yeah, yeah. Last question.

[Lauren Goode]

Okay. I mean, my last question for you is you worked on a product that now, as it has evolved, is in many ways ethically fraught because of some of the harms that people are concerned about with children. When you talk about the fact that there hasn't really been a big breakout consumer app for AI, I think there has. And it's chatbots. Right. And chatbots have also led to some real dangers and harms for young people. And so when you are building in labs, how are you thinking about the potential harms and the risks that come with just making this technology that much better?

[Mike Krieger]

Yeah, I mean, I think there are certainly products that we have either prototyped or conceptualized and been like, this product. This sounds so hypey. I hate this. But this product, if shipped would be bad for the world or would nudge people in the wrong direction or even if we did it right, the wrong or more morally fraught version of this would be actively, we think, bad. And so I think asking that question a lot internally makes a difference. And having it's a luxury to have core products and models that are doing really, really well. So we don't like that's in some ways an easy decision, even if we think it could get a lot of, a lot of use. But yeah, I think going back to an earlier conversation, I think front loading it is really valuable and really thinking through like it is now more normalized to have people at a company and definitely anthropic does where like economists thinking about the impact of the thing that you're building on the world and that just was not the case. And. And along the years on most of

[Alex Cantrowicz]

social media, I think, Mike, it's always great speaking with you. Thank you again for bringing your insight today and let's do it again soon. Let's hear from Mike and Lauren thank you.

[Mike Krieger]

Great job.

[Alex Cantrowicz]

Thank you.

[Mike Krieger]

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