You can watch your favorite TV-programs when you want? With NLZ, you can watch live, back and sometimes even for out. After 7,95 per month. NLZ, slim bekeken. Ready to start your business? Go on the start with the Handels Platform for entrepreneurs. Shopify helps you to start, run and spread your business. With an-in-law-out-of-the-sets, you can build your brand, marketing tools, let your products go up and integrate your business. Google just ran a three-layer strategy move in one day at their Cloud Next conference. They announced new TPU chips. Chrome is turning into an AI co-worker and a multi-billion dollar compute deal with Miriam Moratti, the former co-founder of OpenAI, her company, Thinking Machine Labs. I think this is one of the clearest signals from me so far that Google is structurally perhaps ahead of OpenAI and Amazon in the AI stack. Before that, though, I want to talk about the fact that OpenAI is teaming up with Infosys to get ChatGPT into 60 plus countries of enterprise deals. Bloomberg reports an unauthorized group breached Anthropics' new cyber tool Mythos. And there is a new research lab called Neocognition that has landed $40 million to build agents that actually specialize like humans. So we're going to get into all of that on the show today. Our first story is that 10x Science, this is a Stanford spin-out of a Nobel laureate Carolyn Bertozzi's lab. They just closed a $4.8 million seed, which was led by Initialized Capital. What they're doing that's so fascinating to me is that basically there's this problem where models like DeepMind's Protein Predictor, they're spitting out thousands of drug candidates. So there's just thousands and thousands of these drug candidates. And there's a huge bottleneck in pharma where it's not just about, you know, getting all of these candidates, but it's actually triaging them. It's actually figuring out like which of all the candidates is worth pursuing to try to make medicine or therapeutics. And basically the standard triage tool is just mass spectrometry. So it's very slow. It's very hard to interpret. It's, you know, domain experts only. 10x science is basically just building a SAS layer on top of that. And they have deterministic chemistry plus AI agents to try and make the analysis traceable and explainable, which basically matters because regulators don't accept a black box answer on what the molecule does. Right. So just because an AI model is like, oh, my gosh, we discovered this super cool thing. like this isn't going to fly. Regulators don't want that. There's no, we can't have the black box on the molecules. So we have to be able to fully understand it, fully test it. And knowing which of these, which of these chemicals, which of these drugs to test is a big problem. So everyone in the AI biotech conversation is basically talking about the generative side and almost nobody is building the kind of picks and shovels layer that's underneath of it. And so this is why I think 10X Science is an interesting company. The next thing I want to talk about is neocognition. So this is an AI research lab that came out of stealth with about $40 million in their seed round. Yu Su, an Ohio State professor who runs an AI agent lab there, is the founder. And the round was led by Cambium Capital and Walden Catalyst Ventures. Intel CEO Lit Bu Tang and also Databricks co-founder Ion Stoka both wrote angel checks into this. So I think that's a really strong signal, right, when you have the CEO of Intel, when you have kind of these high-profile co-founders of something like Databricks. This is phenomenal. But essentially the thesis on this company that I think is important is that the current AI agents succeed maybe 50% of the time because they're basically unreliable generalists. And so what they're arguing is that humans aren't great at doing tasks just because we know everything. We're great because we specialize fast when we're dropped into a new domain. So neocognition is trying to build agents that self-specialize the same way instead of kind of the current model where you hand, you know, craft a custom agent for every vertical. I've built enough custom agent workflows to know that kind of this per vertical approach doesn't really scale. You run out of engineers before you run out of use cases So if Neocognition can actually ship an agent that learns the rules of a new environment and if it doing this on its own which I think is definitely going to be the key I think that is a massive win Right now, they have only 15 people on their team. It's mostly PhDs. Definitely an early company, but I think this is one that's worth watching. Okay, so we have some bad news for Anthropic today. There is a report from Bloomberg yesterday that an unauthorized group got access to Mythos, which is Anthropix new exclusive enterprise cybersecurity AI tool that's going to take over the world, right? They gave it to a handful of enterprises because it's so dangerous and good at getting, you know, finding security vulnerabilities. I think this is probably not good news because they, you know, they made a lot of hype about how dangerous it was and they only gave it to special people. And if it really is getting leaked, that's not great for them. I think what basically happened, if you read the reporting on Bloomberg, there was a group that operated out of a private discord channel focused on unreleased AI models and they got credentials from a third-party contractor working with Anthropic. They then guessed the model's endpoint URL based on the pattern that Anthropic uses for other models and that's it. They then gave Bloomberg screenshots and a live demo to prove access. Anthropic's response to all of this is that they're investigating and so far they say they've found no evidence that it touched Anthropic's own systems, just the vendor environment. I think this is interesting because this is kind of where a lot of these security incidents are happening right now. It's not really a model exploit. It's just a contractor credential plus kind of a predictable URL pattern. And my advice that I would give to people is that if you are a company that is running AI tools, make sure that you are auditing who is on your vendor side and who has access to all those tools right now. I kind of the bigger thing is that this is also happening in other places. Anthropic is actively in early talks with Goldman Sachs, JP Morgan, Morgan Stanley about an October IPO. So any sort of bad PR story for Anthropic right now is not going to be great. They definitely want to get on top of this. Next up, I have some OpenAI news. OpenAI and Infosys just announced a deal to push ChatGPT and specifically Codex, their coding tool. It's kind of a competitor to CloudCode, into Infosys's enterprise client base. This is across more than 60 countries. They didn't really disclose the terms or how much Money is changing hands in this. But TechCrunch did report on this earlier today. And basically for context on Infosys, they did about $267 million in AI service revenue last quarter, about 55% of their total. And they play in kind of the massive enterprise transformation deals where Microsoft and Accenture often take the system integrator slot. So right now, Infosys is getting OpenAI models baked into their Topaz AI platform. and OpenAI gets a channel into fortune tier accounts that they don't reach directly. I think OpenAI is trying to make a big push for enterprise right now. I mean, obviously, this has been a goal for a long time, but this big push is coming right now as it feels like Anthropic is really running away with the market in enterprise. I've worked with a lot of enterprises that bought the ChatGPT enterprise licenses and they kind of sat on them for like a year because they didn't wire them into anything real. This is basically the gap that I think Infosys is trying to fill. And strategically, I think this is kind of mirroring what Microsoft already does by bundling Azure OpenAI with Copilot, except Infosys plays in a lot of deals where Microsoft doesn't always win. It's not in the deals. It doesn't have the distribution. So it's kind of a new channel. I would expect to see a deal like this to help push OpenAI's revenue higher. OpenAI obviously wants enterprise revenue to catch up to Anthropic, which is now over $30 billion in annualized revenue. They need this type of distribution in order to try to catch up to Anthropic. Okay, if you're still paying for ChatGPT, Cloud, Gemini, and Grok, by the way, 11 labs for audio and maybe like separate image generators, I have to tell you about AI Box. This is what I personally have built. It's what I recommend to my friends who keep asking me how to actually use AI without spending a ton of money on subscriptions. Basically, AI Box gives you access to over 80 AI models in one place, all of the top models, so you can pick whatever one is best for the task that you're working on or just ask a question and we'll pick the best model for it. The part that I think is super useful is our automation builder. You can just describe a tool that you want in plain English and it will build a workflow for you. So you don need to code You don need to wrestle with a new platform You just describe it and it will build it It a month very cheap I think this saves you a ton of money on just all of the different subscription platforms and you get your hands on every single AI model that is worth using. We have a ton of news from Google. Their Google Cloud Next conference kicked off today and Google did not come empty-handed. They had three major announcements, new TPUs, Chrome is an AI coworker, and a Thinking Machine Labs compute deal. And I think taken together, basically, they're going to make Google the most structurally positioned player in the AI stack right now. I'm going to walk through all of these different deals. But yeah, I've been pretty impressed by their announcements. And of course, we could see other players, but I would definitely say never count Google out in this industry. So first, the chips deal, Google announced two new TPUs, they have the TPU 8T for training and the TPU 8I for inference. The split alone is I think what's really interesting to me. inference is the dominant cost of running AI and production right now and having these kind of dedicated inference silicon matters economically it makes a big impact to the bottom line so google's claims is that it is three times faster at training and 80 better at performance per dollar against the nvidia alternatives and the ability to scale more than a million tpus in a single cluster so they're they're hitting a bunch of different things here but nvidia has traditionally i don't know this is necessarily even nvidia's fault although i'm sure some people will disagree with me here, but NVIDIA traditionally has had a problem with the cluster sizing. And I remember when OpenAI's, or not OpenAI, when XAI did their first like giga terminal, I don't know, whatever, they have a robot name for it. So now I can't remember. But they put together 100 GP NVIDIA, 200,000 NVIDIA GPUs. It was like this big deal. They kind of figured this out and other people started building these bigger and bigger clusters. Well, and it was kind of like they had to figure out a lot of the infrastructure and build it and think about it. Well, it looks like Google is building straight for that. And the ability to have more than a million TPUs in a single cluster is insane. And I mean, insanely scalable. So I think those are Google's own numbers. We don't have any independent benchmarks yet. Google is also not killing the NVIDIA partnership. They're reselling Vera Rubin in Google Cloud later this year. So I think basically Google wants both chips and it wants to let customers choose, which is kind of the opposite of what AWS is all in one training bet is right now. I think Google will actually come out ahead on that. The other thing that I think is interesting that Google announced is that they're turning Chrome into an AI co-worker. So they have a new feature, which is called auto-browse. Gemini powers it, and it's basically running inside of your Chrome for workspace. And it reads context across your open tabs and automates actual workplace tasks. So it can enter your CRM data. It can compare vendor quotes. It can summarize candidate portfolios. It can write up competitor research. If I'm being honest, it sounds like an interesting tool. It sounds useful. And I've actually tried some similar things. Microsoft had something like this in Microsoft Edge a while back where like all of your tabs that are open, it can just get data and read and interact with them, which is super useful. Sometimes, though, you need things that are not, you know, in your tabs. And I think this is where Claude's co-work is still going to keep crushing because they're an app. You give an access to your desktop. It can get files. It can download things on your local device. So I know that like open Google isn't trying to directly compete with that. They're all they're kind of saying, you know, they're just like this human in the loop and they're not kind of letting the agents execute without your approval. And so anyways, I think like they're kind of pitching it as a as a plus. In my personal opinion, it's behind the ball. It's kind of not as good as something like Claude Cowork, which actually sometimes I don't want to have to get 100 pop ups asking for approval. I just wanted to get the job done, especially when I've seen it do it like three or four times successfully. I don't want to approve it. I want it to automate it. And so maybe they're playing it kind of a different target audience there, but I still would probably use Cloud Cowork over that. You can also, something that is very useful though, and kind of like Cloud Cowork, you can save frequent workflows as skills and you can fire them with a forward slash. This is rolling out in the US, workspace users are getting this first. And there's also a commitment that enterprise prompts are not going to be used to train Google's models, which I think is the right call. A lot of enterprises would be very concerned about that especially if they wanted to put confidential data in there But again and not to like I don know say all of the downsides to this Again though you have to go and fire off a skill manually where something like Cloud Cowork you can just set these things up to automatically run Okay the next thing that I thought was interesting is that Google Cloud has just signed a multi billion dollar deal with Miriam Moratti's Thinking Machine Labs. This is a single digit billions, it gives Thinking Machine Labs access to NVIDIA's GB300 system on Google Cloud, plus training and deployment services for their first product, which is called Tinker, which is a tool for building custom frontier models. Thinking Machines is raising at a $12 billion valuation, and they run a heavy reinforcement learning workload, which is basically very insanely compute intensive. Myel Ott, one of the founding researchers, has basically talked a ton about how much Google got them up and running really quickly. So I think this is something that's going to be helpful for their company. The thing that I think ties all of this together is that Google is running a three layer strategy. The bottom layer is silicone, TPU plus these resold NVIDIAs. And then they don't really care which chip wins. I mean, obviously they kind of want their own to win, but they're going to make money either way. If you're going to be buying video from them, the middle layer is being the compute host for frontier labs anthropic already runs on TPUs. And now thinking machine labs is going to be running on Google cloud. The top layer is the agent layer in the browser and the workspace. Google is, you know, the only player that's hitting all three credibly right now. Anthropic is obviously crushing it with end users and, and, you know, kind of in that top layer, but they're not making their own chips, right? And so Google has a big advantage when they own the entire stack. Microsoft is really heavy on opening eye at layer two, but really weak on silicone. Amazon is betting the farm on Anthropic Nvidia is a chip layer and doesn't touch the application layer. Google is basically the only company really doing the full stack. So I think the strongest argument against this is that Gemini still hasn't closed the gap with GPT 5.4 or Claude Opus 4.7 on the benchmarks that consumers actually care about. And so I think, you know, maybe those TPU numbers are Google's own. And you could also say, you know, until like an independent lab publishes real world training runs on the 8T and on the AI, the 3x claim that they've given is marketing. And I mean, I don't want to I don't want to doubt Google too much, but you have to be fair. There is a lot of these AI companies, and Google's been known to do this in the past, to make big claims on marketing, and it doesn't always stack up in the real world. But here's why I still think even with all of that, Google is in an incredible position right now. The customer really doesn't care whether Gemini tops the ELO leaderboards. If they can run inference cheaper on Google stack, and if they can serve it to their employees through Chrome and wire it through their workspace data without having this big, huge system integrator. Structural position, I think matters way more than just pure benchmark lead in the next 12 months. And Google is integrated with every tool that people are using right now. And so I think Google is going to do quite well. The other thing that I think is interesting is that Google is doing this with a DOJ antitrust kind of suit overhanging them right now. The search remedies case that they've been in for a while now is still live and turning Chrome into an agent layer that pulls from workspace data across your browser is basically the exact kind of move that makes regulators pay attention. I think you would, you know, be wise to watch for some comments coming from the DOJ or the EU in the next few weeks. This is going to draw scrutiny that Microsoft and kind of their open AI deal has already been dealing with for a year. What I'm watching personally is the independent TPU 8T and 8I benchmarks, whether auto browse graduates from pilots to real rollouts inside of enterprises and whether more frontier labs migrate to Google cloud. Now that thinking machine is in and anthropic is already there. Um, so I think if even one more lab follows, that is a huge structural shift. All right, that's it for the show today. If you've got something out of this, it would mean the world to me. If you could drop a comment on Apple podcast, or if you could hit the stars over on Spotify, it's the about tab on Spotify where you can, uh, leave a, leave a review, leave some stars, drop a comment on Apple. It helps the show a ton, right? I don't ask for it for nothing. It really helps more people find it. It helps in the algorithm. Also, if you want to get access to over 80 models with an automation builder that runs in plain English for $8.99 a month, go check out AIbox.ai. There's a link in the description. In any case, I'll catch you guys all in the next episode.
