Welcome to the podcast. I'm your host, Jay and Shafer. Today on the show, I want to talk about opening eye fighting back about against the giant onslaught of features anthropic has been pushing some negative PR anthropic has been getting, but at the same time, an incredible new tool called Claude design that just came out. I also want to talk about where some VC dollars are going in the AI space, some surprisingly interesting things there and a new term called token maxing.
In addition, opening eye just massively beefed up codecs for desktop control, memory and in-app browser and over a hundred plugin integration. Basically, this is them swinging directly at anthropics, Claude code and Claude code. I think it matters a lot for where editing agents is going to be in the future. So let's get into it.
Before we do, I wanted to mention AI box. The thing I keep hearing from people is that they're paying for chat, GPT, Claude, Gemini, perplexity, even mid journey. And by the time you get all of that added up, you're likely $70 or $80 a month across a bunch of different logins. AI box gives you access to over 80 different AI models in one interface.
All of this is just $899 a month. If you want to get access to it, there's a link in the description to AI box.ai. And in addition, we have something called the AI box builder, where you can essentially link together multiple AI models. We build up the entire workflow for you and you vibe by build tools without needing to know any code at all.
I'm not a developer. I built this for other people that are not developers. So if you want to check it out, there's a link in the description to AI box.ai. OK, 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. Coach Sula ventures led around Sequoia, insect partners, and Blackstone all were participating in this.
The founder is named Matton Grinberg. He was a physics PhD student at Berkeley. He basically called emailed Sequoia partner Sean Maguire in 2023 and apparently were good friends. They bonded over physics research.
Maguire convinced him to dropout 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. They're not targeting individual developers anyway.
They're focused on the enterprise. Grinberg's pitch is why Factory is kind of differentiated their model flexibility. They basically can let you switch between Claude, Deepseek, 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 random developer tool run inside their network unless it's built with their compliance and security posture in mind. I think that's basically the gap that Factory is filling. And I think $1.5 billion in their current valuation says that VCs are believing there is a real gap year.
Of course, Claude Code is trying to get in there as well. And you can look at things like Cognizant just getting all of their employees, 350,000 of them onto Claude and all the anthropic tools. So I think there's probably competition from a lot of players, but it's interesting that they're carving out a niche there. OK, 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 it's powered by 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 it. You want a pitch deck, a one-page or a landing page prototype, and Claude generates a first draft. You've probably seen it kind of make web pages before. So it's interesting because you actually can use Claude Design to kind of come up with a mock-up 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, I mean, I don't want to throw too much shade at level because I know they do have some direct editing features. It actually never worked super good for me in the past.
Maybe they're better now. But in the past, level would have something where you could 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 sometimes I do the chat after I do that. It would undo them. It would 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 look at this technically, Android is positioning this as kind of complementary to Canva.
They're not just saying, look, we're not going to compete with them. So that's a compliment. The target audience is specifically people who aren't designers. So founders or PM startup operators 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 Co-work, 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 actually own actual workflows and surface area.
It's the same play the opening eyes. 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 showing it, 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.
So yeah, I think we're going to see a lot of these players get more into the software itself beyond just the models, which is pretty interesting. OK, the next thing I want to talk about is token maxing. So there's a funny story on TechCrunch 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 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. So basically, what's going on here is when they're like, look, we're writing all of our code, 50% of our code, a lot of companies.
50% of our code's written by Claude, and it sounds amazing. And it's all perfect and great, or high level of it. And maybe that's true. And I'm not saying that's necessarily that.
I use Cloud Code heavily as well with my startup. But what's interesting is, I think people that pretend it's 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 Farrow AI found code churn increased 861% under high AI adoption. And then you also at Jellyfish, which was looking at 7500 engineers and found that the teams with the biggest token budgets got two times the throughputs at 10 times the token costs. And I think basically it's just a reality check. The productivity gains from AI coding are real.
But they're also a fraction of what the output numbers suggest. If you're like, I can write a million lines of code a day now, and I used to only be able to write this amount. OK, well, you definitely are getting real productivity gains. It's not an argument.
But I just think it's important to check ourselves. And 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. I mean, our normal developer writes code and works on it and optimizes it and fixes it. I think senior engineers, interestingly, are less accepting.
And then AI code and juniors are probably because they know which parts are subtly wrong. So when there's a code push, then it's likely to accept that code push. If you're a manager thinking about how to measure AI ROI, I think counting merged and shipped is important, and not just how much is generated. OK, let's get into physical intelligence.
This is a robotics foundation model startup. It would just publish a 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 examples that are highlighting all this, they have an air fryer. And the robot had only briefly seen this air fryer in training. So it wasn't trained to operate the air fryer in any way.
It briefly seemed to 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 figured out how to operate it. And in some broader testing, the generalist model actually matched specialized models on jobs like making coffee, folding laundry, and assembling boxes.
Research has said that the generalization ability was really surprising to them. 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 out of train fold laundry.
And it does it just as good as the robot, or almost 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 opening iron-thropic that they're building that can do a lot of different things generally good. It's kind of good news for them, because you may not have to have models specifically trained on just a specific task when it's just talks about kind of like physical robotics and stuff.
So on the business side, physical intelligences already raised over $1 billion. They were last valued at $5.6 billion. They're reportedly and talks to nearly double that to $11 billion. They're co-founder, Lacey or Lachi Groom has a track of her back in Figma notion and ramp.
So 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, but I'm trying to be honest, basically Pi is 0.7 still can't handle a lot of multi-step tasks, right? It's not doing this autonomously without any coaching. I think the robotics field doesn't really have a lot of clean benchmarks like LMSU.
You know, we have like humanities less 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 actually working really messy real-world environments. So this is something I'll be closely watching over the next six months or so. OK.
OpenAI has just released a whole bunch of new features to their desktop app, which is called Kodak. Something I've tried in the past, but have opted for, and they're off X-Cloud code in cloud co-work 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 codecs for the first time. So huge upgrade to codecs.
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, codecs can now run in the background on your map, which is phenomenal, right? You 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. Claude sort of does this.
But I'm going to be honest, even with cloud 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, I'm going to be able to work 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 it can't do it in the background and all of a sudden it's clicking on things right in front of me. And I'm like swatting flies, trying to get this thing away.
Well, 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, OpenAI 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 I anthropically 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, or 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 plug-in integrations. So CodeRabbit, GitHub, or GitLab issues. They have a bunch of new exciting things with their memory features.
So if you remember previous sessions, they have an image generation that is now inside of codecs, 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 plug-in ecosystem is probably part of one of the most underrated pieces of this entire announcement because they have like 111 different plug-ins at launch.
They're going to be adding more. And with Claude, Co-work, I mean, it's awesome. But I have like maybe like four things, like Google Calendar and Chrome synced up so a bunch of Google tools and GitHub. But beyond that, there's so many different tools I use that don't integrate very well with it.
It's going to go and use my Chrome browser to access them. So anyways, I think a lot of these integrations that OpenAI is pulling in are going to be very useful. All right, that is the show. If you're getting value from these episodes, please drop a comment over on Apple Podcasts, or leave a couple of stars over on Spotify.
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