Claude vs. Codex: An AI Showdown  🔗 **Source:** [Claude vs. Codex: An AI Showdown](https://share.snipd.com/episode/4dcb7143-a309-4951-b4cb-6c3d5409caf7) ## Highlights - **Factory Raised Big To Serve Enterprise Engineering Teams** - Jaeden describes Factory, an enterprise AI coding startup that raised $150M at a $1.5B valuation. - Factory targets regulated customers like Morgan Stanley and lets teams swap models while offering enterprise compliance posture. Transcript: Jaeden Schafer Okay, the first thing I want to talk about is a company called Factory. So this is an AI coding startup. They're focused specifically on enterprise engineering teams. They just closed $150 million Series A at a $1.5 billion valuation. Koshla Ventures led the round. Sequoia Insight Partners and Blackstone all were participating in this. The founder is named Mattin Grinberg. He was a physics PhD student at Berkeley. He basically cold emailed Sequoia partner Sean McGuire in 2023, and they apparently were good friends. They bonded over physics research. McGuire convinced him to drop out and Sequoia seeded the company. Their customer list already included Morgan Stanley, Ernst& Young, and Palo Alto Networks. So obviously, this is a very enterprise focused, you know, they're not targeting individual developers in any way. They're focused on the enterprise. Grinberg's pitch is why Factory, I think, has kind of differentiated their model flexibility they basically can let you switch between claude deep seek whatever makes sense although Honestly cursor does that too as do i think most of the serious players at this point what i think this does tell us is that even with anthropic and open ai and cursor already in the market Enterprise ai coding still has room for some category specific players morgan stanley isn't going to let some you know random developer tool run inside their network unless it's built With their compliance and security posture in mind. And I think that's basically the gap that factory is filling. ([Time 0:01:29](https://share.snipd.com/snip/98200e49-835e-48f9-bbd0-757bc6f182d4)) - **Anthropic Is Moving From Models To Workflows** - Anthropic is intentionally layering product around models to own workflows, not just APIs. - Claude Design, Cloud Cowork, and agentic plugins signal a play to capture departmental use cases beyond raw model sales. Transcript: Jaeden Schafer Okay. The next thing I want to talk about is a brand new tool from Anthropic called Claude Design. It's a research preview right now. It's available to Pro Max teams and enterprise subscribers and is powered by a claude opus 4.7 the model that just came out a day or two ago so this is what anthropic just shipped and basically You can describe what you want a pitch deck a one pager a landing page prototype and claude generates a first draft you've probably seen it kind of make web pages before um so it's interesting Because you actually can use Claude design to kind of come up with the mockups ahead of time. And then you can refine it by either directly editing it or just talking to it. I actually appreciate both of these options, as I've used a lot of tools, like I mean, I don't want to throw too much shade at lovable, because I know they do have some direct editing features Actually never worked super good for me in the past, maybe they're better now. But in the past lovable would have something where you could, you know, describe the website or whatever you're trying to build, it would generate the design. And then you're supposed to be able to click on it and edit directly. It never worked. And what sometimes I do the chat after I do that, it would like undo them, it just kind of bad. So I think Claude has cracked this a little bit better, maybe levels there as well. But you're able to export as PDFs, URLs, PPTX files, and you can send the outputs straight to Canva. So Canva has a big integration with them. And you can keep all of your collaborations there. It can also read your company's code base and design files to apply a consistent design system across all of your outputs, which is actually I think the more interesting piece if you Kind of look at this technically. Anthropic is positioning this as kind of complimentary to Canva. They're not they're saying like, look, we're not going to compete with them. It's a compliment. The target audience is specifically people who aren't designers. So founders or PM startup operators who need to make something look presentable really fast. What I think about this is that Anthropic is continuing to move up the stack. Earlier this year, they launched Cloud Cowork, then agentic plugins for specific departments. And I think now this, I think where they're going with this is that they're not just trying to be an API company, they want to actually own actual workflows and surface area. It's the same play that OpenAI has been making. And I think you're going to hear more why this matters when we go into the deep dive later on in the episode. But also just shout out to Google, who's been doing this and basically every vertical Google has stitch, which is a very similar design tool as well. ([Time 0:03:15](https://share.snipd.com/snip/5b503716-0697-4811-b451-4e45c45ec983)) - **Anthropic Ships Claude Design For Non-Designers** - Anthropic's Claude Design moves the company up the stack from models to end-user workflows. - It generates mockups, exports to PPTX/PDF/URLs, sends outputs to Canva, and applies a company design system from code and design files. Transcript: Jaeden Schafer Okay. The next thing I want to talk about is a brand new tool from Anthropic called Claude Design. It's a research preview right now. It's available to Pro Max teams and enterprise subscribers and is powered by a claude opus 4.7 the model that just came out a day or two ago so this is what anthropic just shipped and basically You can describe what you want a pitch deck a one pager a landing page prototype and claude generates a first draft you've probably seen it kind of make web pages before um so it's interesting Because you actually can use Claude design to kind of come up with the mockups ahead of time. And then you can refine it by either directly editing it or just talking to it. I actually appreciate both of these options, as I've used a lot of tools, like I mean, I don't want to throw too much shade at lovable, because I know they do have some direct editing features Actually never worked super good for me in the past, maybe they're better now. But in the past lovable would have something where you could, you know, describe the website or whatever you're trying to build, it would generate the design. And then you're supposed to be able to click on it and edit directly. It never worked. And what sometimes I do the chat after I do that, it would like undo them, it just kind of bad. So I think Claude has cracked this a little bit better, maybe levels there as well. But you're able to export as PDFs, URLs, PPTX files, and you can send the outputs straight to Canva. So Canva has a big integration with them. And you can keep all of your collaborations there. It can also read your company's code base and design files to apply a consistent design system across all of your outputs, which is actually I think the more interesting piece if you Kind of look at this technically. Anthropic is positioning this as kind of complimentary to Canva. They're not they're saying like, look, we're not going to compete with them. It's a compliment. The target audience is specifically people who aren't designers. So founders or PM startup operators who need to make something look presentable really fast. What I think about this is that Anthropic is continuing to move up the stack. Earlier this year, they launched Cloud Cowork, then agentic plugins for specific departments. And I think now this, I think where they're going with this is that they're not just trying to be an API company, they want to actually own actual workflows and surface area. It's the same play that OpenAI has been making. And I think you're going to hear more why this matters when we go into the deep dive later on in the episode. But also just shout out to Google, who's been doing this and basically every vertical Google has stitch, which is a very similar design tool as well. ([Time 0:03:15](https://share.snipd.com/snip/07dec015-7fcd-4bde-9b35-3bf6f624cde4)) - **Measure AI ROI By Shipped Work Not Tokens** - Measure AI ROI by merged and shipped code, not just generated tokens or lines. - Jaeden recommends counting durable outcomes because teams with big token budgets saw only modest throughput gains at much higher token cost. Transcript: Jaeden Schafer Okay, the next thing I want to talk about is token maxing. So there's a funny story on on tech crunch recently, where they're talking about token maxing, basically, it's the pattern of companies and developers when they're bragging about How many tokens their AI coding tools burn through, as if you know, the more tokens that they're using means that they're more productive. I think the actual data that they've been crunching is very different. So tools like cloud code cursor codex, they're all generating way more accepted code on the first pass, 80 to 90% of initial acceptance rates. But when you look at the same code two weeks later, the effective acceptance drops to 10 to 30% because engineers were constantly rewriting it, right? So basically, what's going on here is when they're like, look, we're writing like all of our code, 50% of our code, a lot of companies are like 50% of our codes written by like Claude, and It sounds amazing. And again, it's all perfect and great, or, you know, high level of it. And maybe that's true. And I'm not saying that's necessarily bad. I use Claude code heavily as well with my startup. But what's interesting is, I think people that pretend it's, you know, basically a marker of productivity, because what they found, get clear, I was doing this specifically, and they Found that AI users have 9.4 times higher code churn than non-AI users. And Faro AI found code churn increased 861% under high AI adoption. And then you also had Jellyfish, which was looking at 7,500 engineers and found that the teams with the biggest token budgets got two times the throughputs at 10 times the token cost. And I think basically, it's just a reality check, right? The productivity gains from AI coding are real, but they're also a fraction of what the raw output numbers suggest, right? If you're like, I can write a million, you know, lines of code a day now, and I used to only be able to write this amount. Okay, well, the you definitely are getting real productivity gains. It's not an argument. But I think it's important to check ourselves. And, you know, the million lines of code are not all good because if you look at it three weeks later, a big chunk of it has to be rewritten or fixed, which is fine. ([Time 0:05:37](https://share.snipd.com/snip/f9f16391-5915-40fd-ae01-ce95e54f53f6)) - **Token Maxing Inflates AI Coding Productivity** - Token maxing inflates apparent productivity because teams count generated tokens not lasting shipped value. - Studies show AI users have far higher code churn and acceptance falls weeks after generation, so raw token volume misleads ROI measurement. Transcript: Jaeden Schafer Okay, the next thing I want to talk about is token maxing. So there's a funny story on on tech crunch recently, where they're talking about token maxing, basically, it's the pattern of companies and developers when they're bragging about How many tokens their AI coding tools burn through, as if you know, the more tokens that they're using means that they're more productive. I think the actual data that they've been crunching is very different. So tools like cloud code cursor codex, they're all generating way more accepted code on the first pass, 80 to 90% of initial acceptance rates. But when you look at the same code two weeks later, the effective acceptance drops to 10 to 30% because engineers were constantly rewriting it, right? So basically, what's going on here is when they're like, look, we're writing like all of our code, 50% of our code, a lot of companies are like 50% of our codes written by like Claude, and It sounds amazing. And again, it's all perfect and great, or, you know, high level of it. And maybe that's true. And I'm not saying that's necessarily bad. I use Claude code heavily as well with my startup. But what's interesting is, I think people that pretend it's, you know, basically a marker of productivity, because what they found, get clear, I was doing this specifically, and they Found that AI users have 9.4 times higher code churn than non-AI users. And Faro AI found code churn increased 861% under high AI adoption. And then you also had Jellyfish, which was looking at 7,500 engineers and found that the teams with the biggest token budgets got two times the throughputs at 10 times the token cost. And I think basically, it's just a reality check, right? The productivity gains from AI coding are real, but they're also a fraction of what the raw output numbers suggest, right? If you're like, I can write a million, you know, lines of code a day now, and I used to only be able to write this amount. Okay, well, the you definitely are getting real productivity gains. It's not an argument. But I think it's important to check ourselves. And, you know, the million lines of code are not all good because if you look at it three weeks later, a big chunk of it has to be rewritten or fixed, which is fine. ([Time 0:05:37](https://share.snipd.com/snip/6069405b-769c-4ac8-947d-3de3c6a2e5ac)) - **Robotics Model Shows Surprising Zero Shot Generalization** - Physical Intelligence's Pi 0.7 shows strong skill composition enabling zero-shot task performance in robotics. - The generalist matched specialist robots on coffee, laundry, and assembly after only brief exposure to new objects like an air fryer. Transcript: Jaeden Schafer This is a robotics foundation model startup. It just published research on a new model called Pi 0.7. And I think this might be the most novel kind of technical story of the day. But basically, what they're claiming right now is that Pi 0.7 can perform tasks it was never specifically trained on by composing skills it learned in other contexts. So the example that they're kind of highlighting all this, they have like an air fryer and the robot had only briefly seen this air fryer in training. So, you know, wasn't trained to operate the air fryer in any way. It briefly seemed in training and I think it only seemed like two short clips of an air fryer. Then they gave it a step by step verbal instruction and it figure out how to operate it. And in in like some broader testing, the generalist model actually matched specialized models on jobs like making coffee, folding laundry and assembling boxes. Researchers said that the generalization ability was really surprising to them, right? So basically, you train a special, you train the robot specifically to fold laundry, and yeah, it does good. And then they have a new model where it's just kind of a generalist at everything. It's not trained to fold laundry, but you explain step by step how to fold laundry, and it does it just as good as the robot, or almost just as good as the robot that was specifically trained On this. That is fascinating. And I think especially when you look at, you know, I would say, quote unquote, general models like OpenAI or Anthropic that they're building that can do a lot of different things generally Good. It's kind of good news for them because, you know, you may not have to have models specifically trained on just a specific task when he talks about, you know, kind of like physical robotics And stuff. So on the business side, physical intelligence has already raised over a billion dollars. They were last valued at 5.6 billion. They're reportedly in talks to nearly double that to 11 billion. Their co-founder Lacey or Lachey Groom has a track record of backing Figma, Notion and Ramp. I think obviously this is why vc dollars are going to lachi i think something that is interesting a caveat that i'll put on this if i'm trying to be honest like basically pi 0.7 still can't Handle a lot of multi-step tasks right and it's not doing this autonomously without any coaching um i think the robotics field doesn't really have a lot of clean benchmarks like lmsMs Do. You know, we have like humanities last exam and like all these different benchmarks that we give AI models on, you know, like engineering and math and other areas. And we can tell exactly how good they are at those tasks. There's not a lot of that with robotics. I'm sure there's going to be more as we get more into it. So basically for robotics, so you kind of have to just trust whatever their demo is. But I think if this kind of generalized behavior is going to be something that we're looking at, it's pretty significantly stepping us towards robots that actually work in really messy Real world environments. ([Time 0:08:07](https://share.snipd.com/snip/cfbbc5cc-2615-4f84-98f0-eb410d6f35d6)) - **Try Background Desktop Agents For Nonstop Automation** - Use desktop agents that run in the background to automate multi-step workflows without interrupting your work. - OpenAI Codex now operates on Mac background, runs parallel agents, has in-app browser, 111 plugins, memory, and image generation. Transcript: Jaeden Schafer Okay, OpenAI has just released a new a whole bunch of new features to their desktop app, which is called Codex, something I've tried in the past, but have opted for Anthropics Cloud Code And Cloud Cowork in recent weeks in the last month. And I think OpenAI sees that and they really want to make a big push to win people back or to have people try OpenAI Codex for the first time. So huge upgrade to Codex. I'll walk through a couple of things that are new because I think OpenAI is basically swinging directly at Anthropics Cloud Code, which honestly has been really crushing it, right? So this is what they added. First, Codex can now run in the background on your Mac, which is phenomenal, right? It can open up applications. It can click around. It can type into your desktop while you keep working on something else. This is actually something I like. Cloud sort of does this, but I'm going to be honest, even with Claude co-work, a lot of times, if I have like an automated task running, like I've got a bunch of things that I'm just like, You know, every day at 9am do this every day at noon do this. And it's like grabs like analytics or grabs data or goes and gets me a report on something. So actually, the thing that I love using it for is if there's no API for a service, I'll just have it go log into the account and go grab the data I need and bring it back to me hopefully those Companies offer APIs in the future but for now that's what I do in any case it is annoying with cloud co-work that lots of times when those automated tasks start happening all of a sudden This chrome browser pops up on my screen if there's if it can't do it like um in the background and all of a sudden it's like clicking on things right in front of me and I'm like swatting flies Like trying to get this thing away while I keep working on something different should I have its own computer like possibly but a lot of the times I just have it running on the side in any Case open AI is trying to combat that and have it work on things in the background so it's not just writing code in an editor it's actually operating your entire machine so this is what I'm excited about, computer use from OpenAI, which they sort of have done for a long time. They were the OGs way before Anthropic was shifting things in this, but they really just felt stale and bad. I've tried a lot in the past. And trust me, if OpenAI agents back in the day were like six months, a year ago, were as good as what Anthropic's doing now, I'd be shouting them from the rooftops. But it seems like now they're making a comeback. So they can also run multiple agents in parallel without interfering in your desktop, which means that you can have one fixing a bug and you can also have one running tests, one writing Docs all at the same time. They also have a new in-app browser so it can hit web applications directly. They have 111 plugin integrations. So CodeRabbit, GitHub, or GitLab issues. They have a bunch of new exciting things with their memory feature. So it can remember previous sessions. They have an image generation that is now inside of Codex, which to be fair, Claude does not have any sort of image generation. They also rolled out a pay as you go pricing specifically for enterprise and business customers. So well, I think Anthropic is definitely ahead right now, I would say that the plugin ecosystem is probably part of one of the most underrated pieces of this entire announcement, because They have like 111 different plugins at launch, they're gonna be adding more. And with Cloud Cowork, I mean, it's awesome, but I have like, maybe like four things, you know, my Google calendar and Chrome like synced up. So a bunch of like Google tools and GitHub. And, but like beyond that, there's so many different tools I use that don't integrate very well with it. It's got to go and use my Chrome browser to access them. So anyways, I think a lot of these integrations that opening is pulling in are going to be very useful. ([Time 0:10:59](https://share.snipd.com/snip/481fc6e2-79b7-479e-bd34-e1faf156008e))