Anthropic CPO Mike Krieger on Claude Code, MCP, and the Future of AI Product Development

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Transcript

90% of your code roughly is written by AI now.

The team that works in the most futuristic way is the Cloud Code team.

They're using Cloud Code to build Cloud Code in a very self-improving kind of way.

We really rapidly became bottlenecked on other things like our merge queue.

We had to completely re-architect it because so much more code was being written and so many more pull requests were being submitted that it just completely blew out the expectations of it.

You guys are at the edge of where things are heading.

I had the very bizarre experience of I had two tabs open.

It was AI 2027 and my product strategy and it was this like moment where I'm like, "Wait, am I the character in the story?"

It feels like chat GPT is just winning in consumer mindshare.

How does that inform the way you think about product, strategy, and mission?

I think there's room for several generationally important companies to be built in AI right now.

How do we figure out what we want to be when we grow up versus like what we currently aren't or wish that we were or like see other players in the space being?

What's something that you've changed your mind about?

What AI is capable of and where AI is heading?

I had this notion coming in like, "Yes, these models are great, but are they able to have an independent opinion?"

And it's actually really flipped for me only in the last month.

Today, my guest is Mike Krieger.

Mike is chief product officer at Anthropic, the company behind Claude.

He's also the co-founder of Instagram.

He's one of my most favorite product builders and thinkers.

He's also now leading product at one of the most important companies in the world.

And I'm so thrilled to have had a chance to chat with him on the podcast.

We chat about what he's changed his mind about most in terms of AI capabilities in the years since he joined Anthropic, how product development changes and where bottlenecks emerge, when 90% of your code is written by AI, which is now true at Anthropic, also his thoughts on open AI versus Anthropic, the future of MCP, why he shut down Artifact, his last startup, and how he feels about it, also what skills he's encouraging his kids to develop with the rise of AI.

And we close the podcast on a very heartwarming message that Claude wanted me to share with Mike.

A big thank you to my newsletter Slack community for suggesting topics for this conversation.

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With that, I bring you Mike Krieger.

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Mike, thank you so much for being here and welcome to the podcast.

I'm really happy to be here.

I've been looking forward to this for a while.

Wow, I love to hear that.

I've also been looking forward to this for a while.

I have so much to talk about.

So first of all, you've been at Entropic for just over a year at this point.

Congrats, by the way, on hitting the cliff.

Thank you.

Not that we're tracking.

That's right.

So let me just ask you this.

So you've been at Entropic for about a year.

What's something that you've changed your mind about from before you joined Entropic to today about what AI is capable of and where AI is heading?

Two things.

One is like a pace and timeline question.

The other one is a capability question.

So maybe I'll take the second one first.

I had this notion coming in like, yes, these models are great.

They're going to be able to produce code.

They're going to be able to write, hopefully, in your voice eventually.

But are they able to have an independent opinion?

And it's actually really flipped for me only in the last month and only with Opus 4, where my go-to product strategy partner is Claude.

And it has been basically for that full year.

Well, I'll write an initial strategy.

I'll share it with Claude, basically, and I'll have it look at it.

And in the past, it's pretty anodyne kind of comments that it would leave like, oh, have you thought about this?

And it's like, yeah, I thought about that.

And Opus 4, I was working on some strategy for our second half of the year, was the first one.

I was like, Opus 4 combined with our advanced research.

But it really went out for a while and it came back and I was like, damn, you really looked at it in a new way.

And so that's like a thing that I've maybe I didn't feel like it would never be able to do that.

But I wasn't sure how soon it'd be able to like come up with something where I look at it.

I'm like, yep, that is a new angle that I hadn't been looking at before.

And I'm going to incorporate that immediately into how I think about it.

So that's probably the biggest shift that I've had is like, I don't know about independence is the right word, but like creativity and sort of novelty of thought relative to how I'm thinking about things.

And the timeline one, it's like so interesting because, you know, I was sitting next to Dario yesterday and he's like, I keep making these predictions and people keep laughing at me and then they come true.

And it's like and it's funny to have this happen over and over again.

And he's like, not all of them are going to be right.

You know, but even I think as of last year, he was talking about, you know, we're at 50 percent on sweet bench, just like, you know, benchmark around how well the models are at coding.

He's like, I think we'll be at 90 percent by the end of 2025 or something like that.

And sure enough, we're at about 72 now with the new models and we're at 50 percent when he made that prediction.

And it's like continued to scale pretty much like as predicted.

And so I've taken the timelines a lot more seriously now.

And if you read 20 27, I have made my heart race.

Yeah.

And I had the very bizarre experience of I had two tabs open.

It was 20 27 and my product strategy.

And it was this like moment where I'm like, wait, am I the character in the story?

Like, how much is this converging?

But, you know, you read that and you're like, oh, 20 27, that's like that's years away.

If you're like, no, mid 2025 and like things continue to to improve and the models continue to be able to do more and more and they're able to act genetically and they're able to have memory and they're able to act over time.

So I think my like my confidence in the timelines and I don't know exactly how they manifest have definitely just solidified over the last year.

Wow.

I wasn't expecting to go down that because that that paper was scary.

And I'm curious, just I guess I can't help but ask just thoughts on just how do we avoid the scary scenario that that paper paints of where getting really smart goes.

Yeah.

I mean, I this maybe ties into like I've been here a year.

Like, why did I join an anthropic?

I was watching the models get better and even, you know, you could see it in 24 and like, you know, early 2024 and looking at my kids, I'm like, right, they're going to grow up in a world where they it's it's unavoidable.

What is the thing that I can like, where can I maximally apply my time to like nudge things towards going well?

And I mean, that's a lot of what people think about across the industry, especially at anthropic.

And so I think, you know, coming to an agreement and a shared framework and understanding of like, what is going well look like, what is the kind of human relationship that we want?

How will we know along the way?

What do we need to build and develop and research along the way?

I think those are all the kind of key questions.

And, you know, some of those are product questions and some of those are research and interpretability questions.

But for me, it was like the strongest reason to join was, OK, I think there's a there's a lot of contribution that anthropic can have around like nudging things to go better.

And if I can have a part to play there, like let's do it.

I love that answer.

Speaking of kids.

So you've got two kids.

I've got a young kid.

He's just about to turn two.

I'm curious just what skills you're encouraging your kids to build as this, you know, AI becomes more and more of our future.

And some jobs will be changed.

And just what do you what advice do you have?

We have this breakfast, we eat breakfast with the kids every morning and some nice question will come up, you know, like, you know, something about like physics and our oldest kids almost six.

But they ask like funny questions about like, you know, you know, the solar system or physics or, you know, in a six year old way.

And before we reach for Claude, because at first, you know, my instinct is like, oh, I wonder how Claude will do this question.

And like we started changing like, well, how would we find out?

You know, and the answer can't just be the last cloud, you know.

So, all right.

Like, well, we could do this experiment.

We could have this thing.

So I think nurturing curiosity and like still having a sense of, I don't know, the scientific process sounds grandiose to instill in like a six year old.

But like that process of like discovery and asking questions and then, you know, systematically working right through it, I think will still be important.

And of course, AI will be an incredible tool for helping like resolve large parts of that.

But that process of inquiry, I think, is still really important and independent.

That my favorite moment with my kid, because she's very headstrong, our six year old, she's you know, I was like, she said something.

And I was like, I wasn't sure if it was true.

It was, oh, is that coral is an animal or like coral is alive?

And I remember the details of it.

And I was like, I don't know if that's true.

She's like, it's definitely true, dad.

I'm like, all right, like, let's ask Claude on this one.

And she's like, you can ask Claude, but I know I'm right.

And I'm like, I love that.

Like, I want that kind of level of, you know, not just sort of delegating all of your cognition to the, you know, to the eye, because they won't always get it right.

And also kind of like, you know, kind of short circuits, any kind of independent thought.

So the skill of asking questions, inquiry and independent thinking, I think those are all the pieces, what that looks like from a like job or occupation perspective.

Like, I'm just keeping an open mind and I'm sure that'll radically change between between now and then.

It's interesting.

Toby Lutke, Shopify CEO on the podcast.

And he had the same answer for what he's encouraging his kids to to develop his curiosity.

And and so it's interesting.

That's a common thread.

The K through eight school, a kid goes through had an A.I. sort of A.I. an education expert come in and I had a very low bar or like a very low expectations of what this conversation was going to be like.

And actually, I think it went over most of the people in the heads, the audience's heads, because he was like, all right, well, let me take it all the way back to Claude Shannon and information theory.

I could see people's eyes grow like, what did I sign up for?

Why am I here in this school auditorium hearing about information theory?

But he did a really nice job.

I think of also just imagining like, you know, there will be different jobs and we don't know what those jobs are going to be.

And so like, what are the skills and techniques and and remain open mindedness and around like what the what the exact way we recombine those things.

And even those will probably change three times between now and when they're 18.

I want to go back to so we're talking about timelines and how things are changing.

So I've seen these stats that you've shared other folks at Entropic have shared about how much of your code is now written by AI.

So people have shared stats from like 70 percent to like 90 percent.

There is an engineer lead that shared 90 percent of your code roughly is written by AI now, which first of all is just insane that like it went from zero to 90 percent.

I don't know.

A few years, something like that.

I don't think that's I don't think people are talking about this enough.

That's just wild.

You guys are basically at the bleeding edge.

I've never heard a company that is this high a percentage of code being written by AI.

So you guys are at the edge of where things are heading.

I think most companies will get here.

How has product development changed knowing so much of your code is not written by AI?

So usually it's like PM.

It's like here's what we're building.

Engineer builds it, ships it.

Is it still kind of roughly that or is it now PMs are just going straight to Claude?

Build this thing for me.

Engineers are doing different things.

Just what looks different in a world where 90 percent of your code is written by AI.

Yeah, it's really interesting because I think the role of engineering has changed a lot.

But the kind of suite of people that come together to produce a product hasn't yet.

And I think for the worst in a lot of ways, because I think we're still holding on some assumption.

So I think the roles are still fairly similar, although we'll now get in my favorite things that happen now are some nice PMs that have an idea that they want to express or designers that have an idea they want to express.

We'll use Claude and maybe even artifacts to put together an actual functional demo.

And that has been very, very helpful.

No, this is what I mean.

That makes it tangible.

That's probably the biggest role shift is prototyping happening earlier in the process via more of this kind of code plus design piece.

What I've learned, though, is the process of knowing what to ask the AI, how to compose the question, how to even think about structuring a change between the back end and the front end.

Those are still very difficult and specialized skills, and they still require the engineer to think about it.

And we really rapidly became bottlenecked on other things like our merge queue, which is the sort of sort of get in line to get your change accepted by the system that then deploys it to production.

We had to completely re-architect it because so much more code was being written and so many more pull requests were being submitted that it just completely blew out the expectations of it.

And so it's like, I don't know if you've ever read, is it the gold, the classic like process optimization book?

And you realize there's like this like critical path theory.

I've just found all these new bottlenecks in our system.

You know, there's an upstream bottleneck, which is decision making and alignment.

A lot of things that I'm thinking about right now is like, how do I provide the like minimum viable strategy to let people feel empowered to go run and prototype and build and explore at the edge of model capabilities?

I don't think I've gotten that right yet, but it's the thing I'm working on.

And then once the building is happening, other bottlenecks emerge like let's make sure we don't step on each other's toes.

Let's think through all the edge cases here ahead of time so that we're not blocked on the engineering side.

And then when the work is complete and we're getting ready to ship it, what are all those bottlenecks as well?

Like let's do the air traffic control of landing the change.

Like how do we figure out launch strategy?

So I think we're the there hasn't been as much pressure on changing those until this year.

But I would expect that like a year from now, the way that we are like conceiving of building and shipping software just changes a lot because it's going to be very painful to do it the current way.

Wow, that is extremely interesting.

So it used to be here's an idea.

Let's go design it, build it, ship it, merge it and then ship it.

And usually the bottleneck was engineering taking time to build the thing and then design.

And now you're saying the two bottlenecks you're finding are OK, deciding what to build and aligning everyone.

And then it's actually like the queue to merge it into production.

And and I mentioned review it too is probably a part of it.

Reviewing has really changed too.

And in many ways, our most perhaps unsurprisingly, the team that works in the most futuristic way is the Cloud Code team because they're using Cloud Code to build Cloud Code in a very self improving kind of way.

And, you know, early on in that project, they would do very line by line pull request reviews in the way that you would for any other project.

And they've just realized like Claude is generally right and it's producing pull requests that are probably larger than most people are going to be able to review.

So can you use a different Claude to review it and then do the human almost like acceptance testing more than trying to review line by line?

There's definitely pros and cons.

And like so far it's gone well, but I could also imagine it going off the rails and then having a completely both unmaintainable or even understandable by Claude code base that hasn't happened.

But watching them like change their review processes definitely has has been has been interesting.

And yeah, like the merge is one instance of the kind of bottom bottleneck that forms down there.

But there's other ones, which is how do we make sure that we're still like building something coherent and like packaging it up into like a moment that we can share with people and whether that's around the launch moment, whether that's about like then enabling people to use this thing and like talking about it like the classic things of building something useful for people and then making it known that you've built it and then learning from their feedback like still exists.

We've just like made a portion of that whole process much more efficient.

I heard you describe this as you guys are patient zero for this way of working.

Yes, I love that.

Do you have a sense of what percentage of Claude code is written by Claude code?

At this point, I would be shocked if it wasn't 95% plus enough to ask for some other tech leads on there.

But what's been cool is so nitty gritty stuff.

Claude code is written in TypeScript.

It's actually our largest TypeScript project.

Most of the rest of anthropic is written in Python.

Some go some rust now, but it's not, you know, we're not like a TypeScript shop.

And so I saw a great comment yesterday in our slack where somebody had this thing that was driving them crazy about Claude code and they're like, well, I don't know any TypeScript.

I'm just gonna like talk to Claude about it and do it.

And they went from that to pull request in an hour and solve their problem when they like, you know, submitted a full request.

And that kind of breaking down the barriers one, it changes your sort of barrier to entry for any kind of kind of newcomer to the project.

I think you can let you choose the right language for the right job, for example.

I think that helps as well.

But I think it like also just reinforces like Claude code being that patient alpha of that, you know, where like contributions from outside the team can be Claude coded as well.

Wow.

This is just just going to continue to blow my mind.

They got all these things that you're sharing.

Ninety five percent of Claude code is written by Claude code roughly.

That's my guess.

Yeah, I'll come back with the real stuff.

But it's I mean, if you ask the team, that's how that they're working and that's how they're getting contributions from across the company, too.

It's interesting going back to your point about strategy being assisted by Claude itself and your point about how a lot of the bottlenecks now are kind of the top of the funnel of coming up with ideas aligning everyone.

And it's interesting that Claude is already helping with that also of helping you decide what to build.

So if those two bottlenecks are aligning, deciding what to build and then just like merging and getting everything, where do you see the most interesting stuff happening to help you speed those things up?

Yeah, I think that on that on that first run, like I started the year by writing a doc that was effectively like what how do we do product today?

And where is Claude not showing up yet that it should and I think that upstream part is the next one to go.

Interesting.

Like at your conference, I talked to somebody who was working on like a PRD GPT kind of like chat PRD, I think.

Chat PRD.

Yeah.

So, you know, can we push more on, you know, can Claude be a partner in figuring out what to build, what the market size is, if you want to approach it that way, what the user needs are, if you if you look at a different way.

Like we think a lot about the virtual collaborator and topic.

And one of the ways in which I think that can show up is, hey, I'm in the discord, the, you know, the cloud anthropic discord.

I'm in the user for I'm on X and I'm reading things and like here's what's emergent.

That's step one.

Models can do that today.

Step two, which the most probably can do that, which I have to wire them up to do it is like and not only are the problems, here's like how I think you might be able to solve them and then taking that through to like and I put together a pull request to like solve this thing that I'm saying, like feels.

Very achievable this year than stringing those things together.

And we're limited more.

This is why MCP is exciting me.

Like we're limited more around like making sure the context flows through all of that.

So we have the right access to those things more than the model's capability to reason and propose.

Now, the model might not have like perfect UI taste yet.

So there's definitely room for design to intervene and be like, oh, that's not quite how I would solve the problem of this not showing up.

But I know I would get very excited.

I would give you a really small example, but we changed the cloud.

I used to be able to just copy markdown from artifacts or code from artifacts and we changed it so you can actually download it and export it.

We changed the button to export.

We got a bunch of feedback like how do I copy now?

And the answer is like you drop it down and copy.

It's like minding all those things where it's like made sense, but we probably got it like not quite right.

That feedback was in the RUX channel.

Like I would have loved like an hour later for a plot to be like, hey, if we do want to change it back, here's the PR to do it.

And by the way, eventually and then I'm going to spin up an A/B test to see if this changes metrics and then we'll see how it looks in a week.

Like this stuff feels if you told me that about a year and a half ago, like, yeah, maybe like twenty seven, maybe like twenty six.

But it's pretty much like it really feels, you know, just at the tip of capabilities right now.

Wow.

OK, so you mentioned the Lending Friends Summit.

I wanted to talk about this a bit.

So you were on a panel with Kevin Wheel, the CPO of OpenAI.

I think it was the first time you guys did this.

Maybe the last time for now.

Yeah, I haven't done it since, not for any reason.

I had a lot of fun.

What a what a legendary panel we assembled there with Sarah Guo moderating.

And you made this comment actually ended up being the most rewatched part of the interview, which is that you've kind of you were putting product people on the model team and working with researchers, making the model better.

And you're putting some product people on the product experience, making the UX more intuitive, making all that better.

And you found that almost all the leverage came from the product team working with the researchers.

And so you've been doing more into that.

So, first of all, does that continue to be true?

And second of all, what are the implications of that for product teams?

It's continued to be true.

And in fact, I think that the if the proportion was already like skewing towards having more of that embedding, I've just become more and more convinced.

Like I have this I didn't feel as strongly about it during your, you know, the summit.

And now I feel really strongly about it.

Which is if any for shipping things that could have been built by anybody just using our models off the shelf, there's great stuff to be built by using our models off the shelf.

But don't get me wrong.

But like where we should play and like what we can do uniquely should be stuff that's really at that like magic intersection between the two.

Right.

Artifacts can be a great example.

And if you play with artifacts with Cloud four, that's an actually really interesting example where we took somebody from our Cloud skills, which is a team that really is like doing the post training around teaching Cloud, you know, some of these like really specific skills.

And we paired it with some product people.

And then together we revamped how this looks in the product today and like what Cloud can do way better than just like, yeah, we just like use the model and we like prompted a little bit like that's just not enough.

We need to be in that like fine tuning process.

So so much of what you know, if you look at what we're working on right now, what we've shipped recently between like research and all the other things like are things that we like the functional unit of work at anthropic is no longer like take the model and then like go like work with design and product.

And then we're like, oh, you know, if you want to do this memory feature, like we should talk to the researchers because we just shipped a bunch of like memory cards.

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And then we're like, oh, I'm going to do this.

And then we're like, oh, I'm going to do this.

And then we're like, oh, I'm going to do this.

And then we're like, oh, I'm going to do this.

And then we're like, oh, I'm going to do this.

And then we're like, oh, I'm going to do this.

And then the third one is opening people's eyes to what's possible, which is a continuation of making it understandable.

But we were in a demo with a financial services company recently.

And we were working on, here's how you can use our analysis tool and MCP together.

And you could see their eyes light up.

And you're like, ah, OK.

Like, they're still-- we call it overhang, right?

The delta between what the models and the products can do and how they're being used on a daily basis-- huge overhang.

So that's where still a very, very strong necessary role for product.

OK, that's an awesome answer.

So essentially, areas for product teams to lean into more is strategy-- just getting better and better at strategy, figuring out what to build and how to win in the market, making it easier to help people understand how to leverage the power of these tools, the comprehensibility, and along those lines is opening people's eyes to the potential of these sorts of things that's where product can still help.

Exactly.

Awesome.

So kind of along those lines, actually, do you have any just like prompting tricks for people, things you've learned to get more out of cloud when you chat with it?

Sometimes-- and you know, it's funny because we-- in some ways, we have like the ultimate prompting job, which is to write the system prompt for Cloud A.

And we publish all of these, which I think is like another nice area of transparency.

And we are always careful in giving prompting advice because-- at least officially, but I'm going to give you the unofficial version-- because like you don't want things to become like, we think this works, but we're not sure why.

But I'll do small things like in Cloud Code-- and we actually do react to this very literally, but I always ask it to like, if I wanted to use more reasoning, like think hard and it will like use kind of a different flow.

And I would usually start with that.

Nudging-- there's a great essay around like make the other mistake.

Like if you tend to be too nice, can you focus on like-- even if you're trying to be more critical or more blunt, you're probably not going to be the most critical blunt person in the world.

And so with Cloud, sometimes I'm like, be brutal, Cloud.

Like roast me.

Like tell me what's wrong with this strategy.

I think we were talking earlier about the-- you know, Cloud as a thought partner around like critiquing product strategy.

I think I previously would say things like, you know, like what could be better on this product strategy?

I'm just like, you know, just roast this product strategy.

And Cloud's like a pretty nice, you know, entity.

It's not going to be-- it's hard to push it to be super brutal, but it forces it to be a little bit more critical as well.

The last thing I'll say is-- so we have a team called Applied AI that does a lot of like work with our customers around optimizing Cloud for their use case.

And we basically took their insights and their way of working and we put it into a product itself.

So if you go to our console or workbench, we have this thing called the Prompt Improver where you describe the problem and you give it examples.

And Cloud itself will agentically create and then iterate on a prompt for you.

I find what comes out of that ends up being quite different than what my intuitions would have been for a good prompt.

And so I encourage folks to also check that out even for their own use cases because while that tool is meant for an API developer putting a prompt into their product, it's equally applicable for a person doing a prompt for themselves.

Like it'll insert XML tags, which no human is going to think to do ahead of time.

It actually is very helpful for Cloud to understand like what it should be thinking versus what it should be saying, et cetera.

So that's another one is like watch our prompt improver and then note that like Cloud itself is a very good prompter of Cloud.

Awesome.

Okay.

So we're going to link to that, the Prompt Improver.

The core piece advice you shared earlier is just kind of do the opposite of what you would naturally do.

So if you're like trying to be nice, just like be brutal, be like very honest and frank of you.

Exactly.

A friend that works quite well.

Like what are the patterns that I've like fallen into that you want to break me out of?

I saw you guys just today maybe launched a Rick Rubin collab for vibe coding.

Yes.

What's that all about?

That was a, you know, what I've heard about that.

And then ever again, like this, a lot of coalesce this week between model launch developer event and the way of code.

We had our, one of our co-founders, Jack Clark is our head of policy and he got connected to Rick Rubin because I think he's been thinking a lot about coding, the future of coding and creativity and they've stayed in touch.

And, you know, Rick got excited about this idea of like he's creating like art and visualizations with Cloud.

And then he had these like ideas around like the way of the vibe coder and they put together this actually, I love the, I mean, I love almost everything Rick Rubin.

So like the aesthetic of everything is just like so on point too.

But yeah, this is sort of like med, meditation is probably the right word, meditation on like creativity, working alongside AI coupled with this, like, with this like really rich, interesting visualizations.

But it's one of those things where like, you know, internally they're like, oh yeah, and we're doing this like Rick Rubin collab.

We're doing what?

Like that is, that's amazing.

I love the, I looked at it briefly and there's like that meme of him like just like thinking deeply sitting on a computer with a mouth.

Yes.

And like ASCII art, I think.

It's totally, it's like ASCII art five.

I'm excited to have Andrew Luo joining us today.

Andrew is CEO of OneSchema, one of our longtime podcast sponsors.

Welcome, Andrew.

Thanks for having me, Lenny.

Great to be here.

So what is new with OneSchema?

I know that you work with some of my favorite companies like RAMP and Vansa and Watershed.

I heard you guys launched a new data intake product that automates the hours of manual work that teams spend importing and mapping and integrating CSV and Excel files.

Yes.

So we just launched the 2.0 of OneSchema file feeds.

We've rebuilt it from the ground up with AI.

We saw so many customers coming to us with teams of data engineers that struggled with the manual work required to clean messy spreadsheets.

File feeds 2.0 allows non-technical teams to automate the process of transforming CSV and Excel files with just a simple prompt.

We support all of the trickiest file integrations, SFTP, S3, and even email.

I can tell you that if my team had to build integrations like this, how nice would it be to take this off our roadmap and instead use something like OneSchema?

Absolutely, Lenny.

We've heard so many horror stories of outages from even just a single bad record in transactions, employee files, purchase orders, you name it.

Debugging these issues is often like finding a needle in a haystack.

OneSchema stops any bad data from entering your system and automatically validates your files, generating error reports with the exact issues in all bad files.

I know that importing incorrect data can cause all kinds of pain for your customers and quickly lose their trust.

Andrew, thank you so much for joining me.

If you want to learn more, head on over to oneschema.co.

That's oneschema.co.

Actually, going back to kind of the beginning of your journey at Entropic, what's the story of you getting recruited at Entropic?

Is there anything fun there?

It all started, and I actually sent my friend this text.

Joel Lunstein, who I've known, he and I built our first iPhone apps together in 2007 when the App Store was just out.

You could still make money by selling dollar apps on the App Store back in the day.

We were both at Stanford together and we were friends, and we've stayed in touch over years.

We've never gotten to work together since then.

We've just remained close.

I was coming out of the artifact experience.

I was trying to figure out, do I start another company?

I don't think so.

I need a break from starting something from zero.

Do I go work somewhere?

I don't know.

What company would I want to go work at?

He reached out and he's like, "Look, I don't know if you'd at all consider joining something rather than starting something, but we're looking for a CPO.

Would you be interested in chatting?"

At that time, Cloud Three had just come out.

I was like, "Okay.

This company's clearly got a good research team.

The product is so early still."

It was like, "Great.

I'll take the meeting."

First, I met with Danielle.

I was one of the co-founders and the president and anthropic.

Just from the beginning, it was like a breath of fresh air, very little grandiosity coming off the founders.

They're clear-eyed about what they're building.

They know what they don't know.

How many times I talked to Daria, Daria's like, "Look, I don't know anything about product, but here's an intuition.

Usually, the intuition is really good and leads to some good conversation."

Then, they got intellectual honesty and shared view of what it means to do AI in a responsible way, just resonated.

I kept having this feeling in these interviews like, "This is the AI company I would have hoped to have founded if I had founded an AI company, and that's the bar around if I'm going to join something, that should be where I'm going to go."

But what I realized, I actually hadn't joined a company since my first internship in college, basically.

I was like, "Oh, how do I onboard myself?

How do I get myself up to speed?

How do I balance making sweeping changes versus understanding what's not broken about it overall?"

Looking back on a year, I think I made some changes too slowly.

I think there was ways we were organizing a product that I could have made a change earlier.

I think I didn't appreciate how much a couple of really key senior people can shape so much of product strategy.

I'll harken back to Cloud Code.

Cloud Code happened because Boris, who actually was a Boris attorney, he was an Instagram engineer and one of our senior ICs there, we overlapped a bit, started that project from scratch, internal at first, and then we got it out and then shipped it.

That's the power of one or two really strong people.

I made this mistake around, "We need more headcount."

I think there's more work that we need to do and there's things that I want to be building, but more so than that, we need a couple of almost founder-type engineers.

That maybe connects back to our question on what skills are useful and how does product development change.

I still, and maybe even more so, I'm a huge believer in the founding engineer, tech lead with an idea, and pair them with the right design and product support to help them realize that.

I'm ten times more a believer in that than before.

I actually asked people on Twitter what to ask you.

I had this conversation and the most common question surprisingly was, "Why did you shut down artifacts?"

I also wondered that because I loved Artifact.

I was a Power user.

I was just like, "This is exactly, finally, a news app that I love that it's giving me what I want to know."

I guess just what happened there at the end.

I still really miss it too because I didn't find a replacement.

I think I substituted it by visiting individual sites and keeping things up that way and it's not really the same, especially on the long tour.

I think we got right with Artifact.

If people didn't play with it before, we really tried to not just recommend top stories, they were part of it, but really, if you were interested in Japanese architecture, you could pretty reliably get really interesting stories about Japanese architecture every day, whether that's from a dwell or from ArcheticalDitus or from a really specific blog that we found that somebody recommended to us.

It captured some of that Google Reader joy of content discovery of the deeper web.

Our headwinds were a couple.

One of them was just mobile websites have really taken a turn.

I don't blame any individuals for this.

I think it's the market dynamics of it.

We put so much time, our designers, Sky Gunner Grey, who's been nominal.

He's at Perflexity now.

The app experience I was so proud of, but when you click through, it was like, the pressures on these mobile sites and these mobile publishers would be like, "Sign up for our newsletter.

Here's a full-screen video ad."

It was very jarring and we didn't feel like it ethically made sense for us to do a bunch of ad blocking because then you're like, "Sure, you can deliver a nice experience for people," but that doesn't feel like it's playing fair with the publishers.

At the same time, the actual experience wasn't good.

The mobile web deteriorating, which makes me very sad, but I think was part of it.

Two was Instagram spread in the early days because people would take photos and then post them on other networks and tell friends about it.

There was this really natural, "How did you do that?

I want to do it."

News was very personal.

I can't tell you how many people would be like, "I love Artifacts."

I'm like, "Did you tell anybody about it?"

They're like, "I told one person."

It didn't have that kind of spread.

Any attempt that we had to do it felt contrived.

"Oh, we'll wrap all the links in artifact.news."

But we didn't want interstitial things.

In some ways, this sounds very puritanical.

I don't mean it to sound this way, but there were lines that we didn't want to cross because it just felt ethically not us.

I've seen other news players do more of.

Maybe if we had done that, it would have grown more.

But I don't think that's the company we wanted to have built.

I don't think we were the founders to have built it.

The third one, which is an underappreciated one, is we started at mid-COVID, which meant that we were fully distributed.

I think there were major shifts that we would have wanted to make, both in the strategy and the product and the team.

It's really hard to do that if you are all fully remote.

Nothing replaces the Instagram days of, "We went through some hard times."

Like, "Ben Horwood's called the 'We're effed it's over' kind of moments."

My favorite, not just definitely type two fun, I wouldn't say they were my favorite memories because they weren't happy ones, but memories I really stayed with me with Instagram was me and Kevin at Takaria Kankoon on Market Street eating burritos at literally 11 p.m.

being like, "How are we going to get out of this?

How are we going to work through this?"

And that's, you assume, is not a good replica for that.

You tend to let things go or things build up over time.

So the confluence of those three things, we kind of entered, I guess, 2024 and said, "Look, there is a company to be built in this space.

I'm not sure where the people would have built it.

This concurrent incarnation we love, but it's not growing."

The way I put it, it's like 10 units of input in for one unit of output versus the other way around.

We put blood, sweat, and tears into the product and launched something we were proud of and metrics would barely move them.

The energy is not present in this product, in this system.

So are we going to expend another year or two and then go off and fundraise only to find that this is the case?

Or do we call it and see that it's run its course and try to find a home for it, et cetera?

So that was the confluence on it.

And I started feeling this opportunity cost of AI is starting to change everything.

We have an AI-powered news app, but is this the maximal way in which we're going to be able to impact this?

It felt like the answer was increasingly no, but it was hard.

In the end, it was really a piece of the decision, but it was like a conversation that went on for a couple of months.

On that note, just how hard was it?

Because there's an ego component to it like, "Oh, I'm starting my new company.

It's going to be great."

And then you end up having to shut it down.

Just how hard is that as a very successful previous founder shutting something down and then not working out?

Yeah, I think when we started it, one of the conversations was like, "What is the bar to success here?

And do we want it to be something other than Instagram DAU?"

Which is just an impossible bar.

Only one company since then, maybe two, right?

You could say maybe Chatchee V.T.

and TikTok have reached that kind of mass consumer adoption.

Starting a news app, most people are not daily news readers even.

And so we knew that we weren't pursuing that size of usage, at least with the first incarnation.

But we did have an idea of building out complementary products over time that all use personalization and machine learning.

We didn't even call it AI at the time.

It was 2021 back then.

Yeah, AI was called machine learning back then.

Yeah, it was called machine learning still.

And so in shutting it down, you know, it's like...

You kind of know it when you see it in terms of user growth and traction.

And I wasn't expecting Instagram growth, but I was expecting or hoping for or looking for something that felt like at its own legs under it and it could continue to compound.

I was really positively surprised by how supportive people were when we announced it.

There was very little...

There was a bit of like, "I told you so."

It's like, "Sure, anything launching you could be like, this is not going to work."

And you're right most of the time because most things don't work.

There was actually very little of that.

And most people, the universal reception, at least as I received it, was kudos for calling it when you saw it and not like kind of protracted, you know, doing this for a long time.

And I've talked to founders since then that have been like, "Yeah, I probably would have taken this thing out for another six months."

But saw what you guys did, realized we were barking up the wrong tree, made the call.

And I was like, "If that frees up people to go work on more interesting things, I feel like that's like a good legacy for artifact to have."

But for sure, that was like an ego bruise of, "Oh, you know, like, are people...

Is it true that you're only as good as your last game, you know, if I am a huge sports fan?"

So like, is that true or, you know, is there something more of a time?

I'm very competitive, but primarily with myself.

And so I'm always trying to find the next thing that I want to go and do that's hard.

And unfortunately, that probably means that more often than not, I'll feel dissatisfied with the most recent thing that I did, but hopefully that yields good stuff in the end.

Yeah, I think just the trajectory you went on after shows that it's okay to shed down things that you were working on.

Okay, so you mentioned chat GPT.

I wanted to chat about this a bit.

So there's something really interesting happening.

On the one hand, you guys are doing some of the most innovative work in AI.

You guys launched MCP, which is just like, I don't know, the fastest growing standard of any time in history that everyone's adopting.

Claude powered and unlocked essentially the fastest growing companies in the world, Cursor, Lovable, and Bolt, and all these guys.

I had them on the podcast and they're all like, "When Claude, I think 3.5 came out, saw it."

It was just like, "That's all made this work, finally."

On the other hand, it feels like chat GPT is just winning in consumer mind share when people think AI, especially outside tech, it's just like chat GPT in their mind.

So let me just ask you this.

I guess first of all, do you agree with that sentiment?

And then two, as a kind of a challenger brand in the AI space, just how does that inform the way you think about product, strategy, and mission, and things like that?

Yeah, I mean, you look at the sort of public adoption or like you ask people like, "Oh, if you're Jimmy Kimmel, man on the street kind of thing, name an AI company."

I bet they would name.

And actually, I'm not even sure they name open AI.

They'd probably name chat GPT because that brand is the kind of lead brand there as well.

And I think that's just the reality of it.

I reflect on my year.

I think maybe two things are true.

One is like consumer adoption is really lightning in a bottle and we saw it at Instagram.

So like almost maybe more than anybody, I can look internally and say like, "Look, we'll keep building interesting products."

One of them may hit.

But to kind of craft an entire product strategy around like trying to find that hit and is probably not wise, we could do it.

And maybe cloud can help come up with the fullness of things.

But I think we'd miss out on opportunity in the meantime.

And then instead, look yourself in the mirror and embrace who you are and what you could be rather than like who others are is maybe the way I've been looking at it, which is if a super strong developer brand, people build on top of us all the time.

And I think we also have like a builder brand, like the people who I've seen react really well to cloud externally.

Maybe the Rick Rubin connection has some resonance here as well.

Like can we lean into the fact that like builders love using cloud.

And those builders aren't all just engineers and they're not just all entrepreneurs starting their company, but they're people that like to be at the forefront of AI and are creating things.

Maybe they didn't think of those as engineers, but they're building.

I got this really nice note from somebody internal on the topic who's on the legal team.

And he was building like bespoke software for his family and connected them in a new way.

And I was like, this is a glimmer of something that we should lean into a lot more.

And so I think what I've, and this is actually connecting back to what I was saying, like cloud's being helpful here.

Like a lot of what I've been thinking about like going into the second half of the year and beyond is like, how do we figure out what we want to be when we grow up versus like what we currently aren't or wish that we were or like see other players in the space being.

I think there's room for several like generationally important companies to be built in AI right now.

That's almost a truism given like the sort of adoption and growth that we've seen, you know, at Anthropic, but also across open AI and also places like Google and Gemini.

So like let's figure out what we can be uniquely good at that place to the personality of the founder.

Like this, all the things come together, right?

Like the personality of founders, the like quality of the models, the things the models tend to excel at, which is like agentic behavior and coding like great.

Like there's a lot to be done there.

Like how do we help people get work done?

How do we let people delegate hours of work to cloud?

And maybe there's fewer like direct consumer applications on day one.

I think they'll come, but I don't think that like spending all of our time focused on that is the right approach either.

And so it's, you know, I came in, everybody expected me to just like go super, super hard on consumer and make that the thing.

And again, with make the other mistake, instead I spent a bunch of time talking to like financial services companies and insurance companies and like others to like, we're building on top of the API.

And then lately I spent a lot more time with startups and seeing all the people that have grown off of that.

And I think the next phase for me is like, let's go spend time with like the builders, the makers, the hackers, the tinkerers, and like make sure we're serving them really well.

And I think good things will come from that.

And that feels like an important company as we do that.

So essentially it's differentiate and focus, lean into the things that are working.

And don't try to just like beat somebody at their own game.

Exactly.

Super interesting.

So kind of along those lines, a question that a lot of AI founders have is just like, where's a safe space for me to play where the foundational model companies are going to come squash me?

So I asked Kevin Wheel this and he had an answer.

And I noticed looking back at that conversation, he mentioned Windsurf a lot.

He was like, wow, let's get really left to Windsurf.

And then like a week later, they bought Windsurf.

So it all makes sense now.

And the question just is just where do you think AI founders should play?

Where they are least likely to get squashed by folks like OpenAI and then Throbic?

And also, are you guys going to buy Cursor?

I don't think we're going to buy Cursor.

Cursor is very big.

We love working with them.

A few thoughts on this.

And it's a question I've gotten.

We like to do these kind of founder days with whether it's Menlo Ventures, and I just know where it's like we've done YC.

We've done these like founder days.

And it's like the question that is on all of these founders minds, understandably.

So I think things that are going to-- I can't promise this as like a five to ten year thing, but at least like one to three years, things that feel defensible or durable.

One is understanding of a particular market.

I spend a bunch of time with the Harvey folks and they really like-- they showed me some of their UI.

I'm like, what is this thing?

They're like, oh, this is a really specific flow that like lawyers do.

Like you never would have come up with it from scratch.

And it's like not like you could argue about whether it's like the optimal way they get things done, but it is the way that they get things done.

And here's how I can like help with that.

And so like differentiated industry knowledge biotech.

I'm excited to go and partner with a bunch of companies that are doing good stuff around AI and biotech.

And we can supply the models and some applied AI to help make those models go well.

And like I've been dreaming about like at what point do this live equipment, I'll get an MCP and that you can then drive using cloud.

Like there's all these cool things to be done there.

I don't think we're going to be the company to go build the intense solution for labs, but I want that company to exist and I want to partner with it.

You know, domains like legal again, healthcare.

I think there's a lot of like very specific kind of compliance and things.

These are things that necessarily sound sexy out the gate, but there are like very large companies to go and be built there.

So that's number one.

Paired with that is like differentiated go to market, which is the relationship that you have with those companies, right?

Like do you know your customer at those companies?

Like one of our product leads, uh, Michael is always talking about like, no, not, don't just know the company you're selling to, but know the person you were selling to at the company.

Are you selling to the engineering department?

Cause they're trying to like pick which AI LLM to build on top of or API to build on top of.

Let's go talk to them.

Like, is it the CIOs, the CTO is the CFO is that the like general counsel.

So under like a company is with deep understanding of who they're selling to is, is the other piece to what's, you know, what's interesting there is it's, it's probably hard to build that empathy in a three week or three month accelerator, but you've maybe been starting having that first conversation and build that out or maybe you came from that world or you're co-founding somebody who came from that world.

Then the last one is like there's tremendous power and distribution and reach to being chat GPT and having, you know, hundreds of millions or billions of users.

Like, uh, there's also like people have an assumption about how to use things.

And so I get excited about startups that will get started that have like a completely different take on what the form factor is and by which we interface with, with AI.

And I haven't seen that many of them yet.

I want to see more of them.

I think more of them will get created with, with, uh, some things like our new models, but the reason that that's an interesting space to occupy is like do something that feels like very advanced user, very power user, very like weird and out there at the beginning, but could become huge if the models make that, you know, easy and main it's hard for existing incumbents to adapt to because people already have an existing assumption about how to use their products or how to adapt to them.

So those are my answers.

I don't envy them.

Like I would probably be asking those questions if I was starting a company in, in, in the AI space, maybe the part of the reason why I wanted to join a company rather than start one, but I still think that there are there's.

And maybe like here's fourth, like don't underestimate how much you can think and work like a startup and feel like it's you against the world.

It's existential that you go solve that problem and that you go build it.

It sounds a little cliche, but it's like, it's all we had at Instagram.

You know, we were two guys and we're like, let's see what we can do in an artifact.

We were, you know, we were six people, uh, for most of that time.

And you know, every day felt like it's existential that we get this right.

We need to, to win.

And you can't replicate that and you can't instill that with okay.

It's like, you just have to feel it and that is a way of working rather than a like area of building, but it's a continued advantage.

If you can harness it.

I love that you still have such a deep product founder sense there as you're building products for this very large company now kind of on the flip side of this people working with your models and API.

So I imagine there's some companies that are finding ways to leverage your models and APIs to their max and are really good at maximizing the power of what you guys have built.

And there's some companies that work with your APIs and models that happen to figure that out.

What are those companies that are doing a really good job building on your stuff, doing differently that you think other companies should be thinking about?

I think being willing to build, um, more at the edge of the capabilities, um, and basically break the model and then be surprised by the next model.

Like I love that you, you said that the companies were like three, five is the one that finally made them possible.

Those companies were trying it beforehand and then hitting a wall and being like, oh, the models are like almost good enough for, they're okay for this specific use case, but they're not generally usable and nobody's going to adopt them, you know, universally, but maybe these like real power users are going to try it out.

Like those are the companies that I think continuously are the ones from like, yep, like they get it.

They're really pushing forward.

We ran a much broader early access program with these models than we had in the past.

And part of that was because there's this real, like, you know, we can hill climb on these evaluations and talk about sweet bench and tile bench and terminal bench, whatever.

But customers ultimately know like, you know, cursor bench, which doesn't exist other than in, you know, their usage and their own testing, et cetera, is like the thing that we ultimately need to serve, not just cursor, but Manus bench, right?

If Manus is using our models and Harvey bench, like those things and customers know way better than anybody.

And so I would say that's two things.

Like one is pushing the frontier of the models and then having a repeatable process.

This actually goes back to our summit conversation, like a repeatable way to evaluate how well your product is serving those use cases and how well if you drop a new model in, is it doing it better or worse?

Some of it can be classic AB testing.

That's fine.

Some of it may be internal evaluation.

Some of it may be capturing traces and be able to rerun them on with a new model.

Some of it is vibes.

Like we're still pretty early in this process and some of it is actually trying it.

And being one of my favorite early access quotes was the founder heard this engineer screaming next to him.

What is this model?

Like it's like, I've never seen this before.

It's like Opus 4.

I was like, cool, like that.

We're going to generate that feeling and things, but you're not going to be able to feel that unless you have a really hard problem that you're asking the model repeatedly.

So those are the things that I think kind of differentiate those, those companies that are really earlier in their journey of adoption versus the the later ones.

I can't help but ask about MCP.

I feel like that's just so hot and just like Microsoft had their announcement recently, they're like, that's part of the OS.

Just what role do you think MCP was will play in the future of product going forward of AI?

I think as the non-researcher in the room, I get to have fake equations rather than real ones.

And my like fake equation for like utility of AI products.

It's three part.

One is model intelligence.

The second part is context and memory.

And the third part is like applications in UI.

And you need all three of those to converge to actually be a useful product in in AI and model intelligence.

We've got a great research team.

They're focused on it.

There's great, great models being released.

The middle piece is what MCP is trying to solve, which is for context and memory, like the difference between I'll go back to my product strategy example, like, hey, like, you know, let's talk about topics, product strategy.

It's going to maybe go out on the web, like versus here's like several documents that we worked on internally and then, you know, use MCP to talk to our Slack instance and figure out what conversations are happening and then like go look at these documents in Google Drive, like that the difference between like the right context and it's like the entirely the difference between like a good answer and a bad answer.

And then the last piece is, are those integrations discoverable?

Is it right?

Is it easy to like create repeatable workflows around those things?

And that's like, I think a lot of the interesting product work to be done in AI, but MCV really tried to tackle that middle one, which is we started building integrations and we found that every single integration that we were building, we were rebuilding from scratch in a non sort of repeatable way.

And like full credit to two of our engineers, Justin and David, and they said, well, you know, what if we made this a protocol and what if we made this something that was repeatable?

And then let's take it a step further.

What if instead of us having to build these integrations, if we actually popularize this and people really believe that they could build these integrations once and they'd be usable by Claude and eventually chat GPT and eventually Gemma, it was like the dream.

Like when, when more integrations get built and wouldn't that be good for us?

You know, I think channeling a lot of, um, it's like an old, uh, commoditize your compliments, Joel Spolsky essay, you know, it's like, we're building great models, but we're not an integrations company.

And, you know, we're, as you said, the challenger, like we're not going to get people necessarily building integrations just for us out of the gate.

Unless you're like a really compelling product around that.

But MCP really inverted that, which was, you know, it didn't feel like wasted work and, and a few key people like Toby, I think is a great example.

A Shopify got it.

Kevin Scott at Microsoft is like been really a, just an amazing champion for, for MCP and a thought partner on this.

And, um, I think the role going forward is can you bring the right context in?

And then also, you know, once you get, as the team calls it internally, like MC pill, like once you start seeing everything through the eyes of MCP is like, I've started saying the things like guys, we're building this whole feature, like this shouldn't be a feature that we're building.

This should just be an MCP that we're exposing.

Like a small example of like how I think even anthropic could be a lot more MCP pilled, if you will, is like, you know, we've got these building blocks in the product, like projects and artifacts and styles and conversations and groups and all of these things.

Those should all just be exposed in MCP.

So Claude itself can be writing back to those as well.

Right?

Like you shouldn't have to think about like, uh, uh, watch my wife out of conversation with the other day.

And she was, she found, she had generated some good output and she's like, great.

Can you add it to the project knowledge?

And Claude's like, I sorry, Dave, I can't help you with that.

And like, it would be able to, if every single primitive in cloud AI was also exposed to the MCP.

So I hope that's where we had, and I hope that's where more things had, which is to really have agency and have these agentic use cases.

Like one way you approach it is computer use, but computer use has a bunch of limitations, but we, I get way more excited about everything is an MCP and our models are really good at using MCPs.

All of a sudden, everything is scriptable and everything is composable and everything is usable identically by these models.

That's like, that's the future I want to see.

The future is wild.

Okay.

So to start to close off, I close out our conversation, uh, make it a little more, a little delightful.

I, I was chatting with Claude actually about what to talk to you about.

I was just like, Claude, your, uh, your boss is coming on my podcast.

He builds the things that people use to talk to you.

What are some questions I should ask him?

And then also do you have a message for him?

I love this.

Okay.

So first of all, interestingly, when I was using 3.7 to do this and I asked it this and, and by the way, is Claude, is there genders like he, she, they, what do you hope to do?

It's definitely it internally.

I've heard people do they, I got my first, uh, he the other day and I got somebody who was like her and I was like, interesting, but yeah, I'm usually it.

They, okay.

Okay.

Cool.

So, uh, interestingly, 3.7, all the questions were at Instagram and I was like, no, no, he's CPO of anthropic.

And it's like, he's not affiliated with anthropic.

And I was like, he is.

And it's like, okay, here's the questions, but 4.0 nailed it from the start.

So I read the questions and it nailed it.

Okay.

So two questions from Claude to you.

Uh, one is, uh, how do you think about building features that preserve user agency rather than creating dependency on me?

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When you're making hard product decisions, remember the quiet moments matter too.

The person working through grief at 3am, the kid discovering they love poetry, the founder finding clarity and confusion, not everything meaningful shows up in metrics.

That's beautiful.

It resonates so much with me.

Like, a thing I love about the kind of approach we've taken to training Claude, and it's like partly the constitutional AI piece, and it's partly just the general like sort of vibe and taste of the research team is it does like it's little things like sometimes it'll be like, man, I'm sorry you're going through it doesn't say man, but like the effect of like man, you're so sorry you're going through that, you know, like, oh, like that sounds really hard.

It doesn't feel fake.

It feels like just a natural part of the response.

And I love that focus on those small moments that don't, you know, they're not going to show up and necessarily the thumbs up thumbs down data.

I mean, sometimes they do, but it's not like an aggregate stat that you wouldn't even want to optimize for it.

You just want to feel like you're training the model that you like, hope would show up in people's lives.

Well, you're killing it, Mike, a great work.

I'm a huge fan.

We're gonna skip the lighting around just one question.

How can listeners be useful to you?

Oh, I love places where like it goes back to that founder question around building at the edge of capability, like what are you trying to do with Claude today that Claude is failing at is the most useful input I could possibly have, you know, so DM me I love hearing the like, oh, it's like, it's falling on this thing I had to run for an hour and it fell over.

I'm trying to use cloud AI for this, but you know, got a ping from somebody that like, you just made a project API, I've used Claude every day because I want to upload all this data, you know, automatically.

It's like, okay, great.

Like this, I love that.

Like, tell me what sucks.

Amazing, Mike, thank you so much for being here.

Thanks for having me, Lenny.

Bye, everyone.

Thank you so much for listening.

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