← The AI Daily Brief: Artificial Intelligence News and Analysis
The AI Daily Brief: Artificial Intelligence News and Analysis
The 7 Most Important Things We Learned About AI This Week
00:19:51
Loading summary
Transcript1 lines
- [00:00]
A
Today we are talking about the seven most important things we learned about AI this week. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. All right friends, quick announcements before we dive in. First of all, thank you to today's sponsors, Blitzy Rovo, Landfall IP and Robots and Pencils. To get an ad free version of the show, go to patreon.com aidaily brief or you can subscribe on Apple Podcasts. If you are interested in sponsoring the show or really learning anything else about it, visit us at aidailybrief AI or send us a note at sponsorsi dailybrief AI lastly, it is the last couple days of our AI ROI Benchmarking study. Get in your use cases by Tuesday to get the full report in a few weeks when it is ready. All right friends, well welcome back to another weekend episode, which means it's a big think long reads type of episode. But as I sometimes do, after a big week or a couple of weeks, instead of reading something else or even basing this off of someone else's thoughts, I'm just going to get a little bit extemporaneous about what I think are the things that we learned this week about the AI space. In particular what I think are the most important things we learned about the AI space. Now there's a lot more, but these are the seven things that stood out to me when I was driving home from New York last night thinking about all of the implications of really not just the last week, but the last couple of weeks. I think that we will look back on this couple week period as wildly significant, both on a very personal and professional level in terms of the capabilities increase that all of us now have access to, but also in terms of the dynamics in the AI race. And that is where we will start. First, most important thing that we learned about AI this week Google is a player and Sam Altman and OpenAI are worried. Now obviously Google was a player before this, but their return to the top of the heap has been something to watch. It wasn't all that long ago that they were caught totally behind and kind of embarrassed by OpenAI and the launch of ChatGPT, only to launch a very dubious product in Bard, which then gave way to the first rushed versions of Gemini, which had all sorts of problems, including wild recommendations in AI overviews and AI search, as well as some very questionable choices in terms of historical accuracy when it came to image generation. And so you had like an 18 month period there where that was what people were thinking of when they thought about Google and AI. Things started to shift, of course, with the release of Notebook lm. For the first time in a long time there was an AI consumer product that people really genuinely loved. Now, specifically it was the Audio overviews feature that really captured people's attention. But it turned out that it wasn't just the novelty of the audio overviews. The entire suite was really useful and to the extent that Audio overviews were the thing that got people into Notebook, they stuck around for a variety of other features which have continued to evolve. That's kind of where we were heading into this year. Now the 2.5 series of models were really good. Flash was incredibly useful from both a speed and a cost perspective, and Pro contended with the other models at the top of the pile on a lot of different types of use cases. Obviously. However, the launch of Gemini 3 and the Companion launch of Nano Pro has really put Google into the stratosphere and completed this three year return to form journey that they've been on. One interesting thing that was dug up by the Information this week was that in advance of Gemini 3, OpenAI boss Sam Altman had actually sent a memo to his team about what he anticipated to be rough seas ahead. According to the information, OpenAI researchers had discovered or heard that Google had created new AI that had, in their words, leapfrogged OpenAI's in the way that it was developed. Altman said that their recent progress in AI could, quote, create some temporary economic headwinds for our company. He said, we know we have some work to do, but we're catching up fast. And he cautioned employees that he, quote, expected the vibes out there to be rough for a bit. Now the broader story here is the competition coming from all sides for OpenAI right now. As the Information points out, Anthropic has done a tremendous job this year, increasing their revenue from developer focused use cases as well as their API. You've got Google surging even before the release of Gemini 3 and Nano Banana Pro, with their Gemini app reaching number two and even at one point beating out ChatGPT as the top free app and hitting 650 million monthly users. Now in that memo, Altman recognized that OpenAI still does have a brand advantage. He said ChatGPT is AI to most people and I expect that to continue, but it's no doubt that the company is heading into a more difficult period now. One thing that is positive for the field as a whole, even if it does put Competitive pressure on OpenAI is what the launch of Gemini 3 suggests about pre training and scaling laws. In short, the argument that we've hit a performance plateau or a wall looks a lot more dubious today than it did about a week ago after Gemini 3 was released and shared all of its impressive benchmarks, including a few that saw just incredibly big jumps, such as its Screen Understanding benchmark, which more than doubled the previous state of the art. Google DeepMind's Oriole Vignals writes, The secret behind Gemini 3 is simple improving pre training and post training. Contra the popular belief that scaling is over, the team delivered a drastic jump. The delta between 2.5 and 3.0 is as big as we've ever seen. No walls in sight now, after OpenAI responded to the launch of Gemini 3 with GPT 5.1 Codex Max and GPT 5.1 Pro, their researcher Noam Brown said something similar. He wrote, today we are releasing GPT 5.1 Codex Max, which can work autonomously for more than a day over millions of tokens. Pre training hasn't hit a wall and neither has test time Compute Now. Oriole actually was talking about this as well, that it wasn't just pre training, but also post training and all the strategies that we have after the model has been trained to get more performance out of it. Indeed, Oriole called post training a total greenfield. He said there's lots of room for algorithmic progress and improvement and 3o hasn't been an exception. Now, this is all good news for a number of reasons. Altman seemed to acknowledge this in that note, saying at one point, by all accounts, Google has been doing excellent work recently. The information points out, Google's success with pre training in particular came as a surprise to many AI researchers, given that OpenAI at times has struggled to eke out gains from pre training, an issue Google also wrestled with for a while, apparently. By the way, OpenAI has a new LLM that is codenamed Shallow Pete that takes a different approach to pre training and fixes bugs that they had previously encountered. Still, moving back to the implications of the models that were released this week, not only is it good news for consumers that there's more gains to be had, it's also good news for investors who are betting on the AI theme. One of the biggest things that AI bears bring up is the potential that we run into these sort of performance plateaus and walls that ultimately also lead to a plateau in demand below the rate where it would sustain all of these big infrastructure buildout deals that have been signed in the recent months and in fact it seems like there is still room to run is genuinely a good thing for basically everyone in this space and all the consumers who are using these tools now. Moving away from just Gemini 3 strictly into nano Banana Pro as well, but abstracting it a little bit, it does feel like you're sort of starting to see the resource advantage that Google has show up. The information again points out the disparity they wrote OpenAI is one of the fastest growing businesses in history, going from next to no revenue in 2022 to a projected 13 billion this year. By the way, Sam Altman says that that's actually closer to 20 billion. They continue, but it is also projected it would burn more than 100 billion in pursuit of human level AI in the coming years, while spending hundreds of billions of dollars to rent servers to do it, meaning it will likely need to raise the same amount in additional capital. Meanwhile, Google, valued at 3.5 trillion, generated more than 70 billion in free cash flow over the past four quarters alone. While ChatGPT looks poised to take a bite out of Google search, Google's financial performance has improved in parts because it also has a booming cloud business that rents out servers to large customers, including OpenAI and Anthropic. The financial disparity between OpenAI and established firms like Google has prompted public market investors to question whether the startup's unprecedented revenue growth, including projected growth, will be enough to erase concerns about its future cash burn. Now, hold aside whatever the market is thinking about this, because frankly, I care a lot less about that. I think where you're seeing the resource advantage show up is in and around multimodal. Google is not just flexing with their core model, they're also flexing the things around it. We haven't had an Update about an OpenAI image generation model for months and months and months, unless you consider Sora 2 as part of that. Whereas Nano Banana and now Nano Banana Pro are out here really, really transforming what it seems like is possible with image generation. The reason that Google is able to do multiple things at once is is that resource advantage, and I wonder how that's going to start to create more and more distance and space between them and competitors. Remember, Anthropic at some point decided not to even compete on that multimodal dimension. And I think the question that some will ask is will OpenAI have to make similar types of decisions? This episode is brought to you by Blitzy, the enterprise autonomous software development platform with infinite code context. Blitzy uses thousands of specialized AI agents that think for hours to understand enterprise scale codebases with millions of lines of code. Enterprise engineering leaders start every development Sprint with the Blitzi platform, bringing in their development requirements. The Blitzi platform provides a plan, then generates and pre compiles code for each task. Blitzi delivers 80% plus of the development work autonomously while providing a guide for the final 20% of human development work required to complete the Sprint. Public companies are achieving a 5x engineering velocity increase when incorporating Blitzi as their pre IDE development tool, pairing it with their coding pilot of choice. To bring an AI native SDLC into their org, visit blitzi.com and press get a demo to learn how Blitzy transforms your SDLC from AI assisted to AI native. Meet Rovo, your AI powered teammate Rovo Unleashes the potential of your team with AI powered search, chat and agents or build your own agent with Studio. Rovo is powered by your organization's knowledge and lives on Atlassian's trusted and secure platform, so it's always working in the context of your work. Connect Robo to your favorite SaaS app so no knowledge gets left behind. Robo runs on the Teamwork graph, Atlassian's intelligence layer that unifies data across all of your apps and delivers personalized AI insights From day one. Rovo is already built into Jira Confluence and Jira Service Management Standard Premium and enterprise subscriptions. Know the feeling when AI turns from tool to teammate? If you Rovo, you know. Discover Rovo, your new AI teammate powered by Atlassian. Get started at ROV as in victory o.com if you're listening to this, you already know how fast AI is writing the rules for innovation, disruption and value creation. And this new era demands a new kind of patent law firm. Landfall IP was built from the ground up to operate differently, orchestrating how human expertise and AI work together for better patents at founder speed. Created by world class patent attorneys who saw a better way, Landfall IP lets AI execute the repeatable while attorneys elevate to create the exceptional. Landfall isn't adapting to AI, they were built for it. Have a new idea? Try the Discovery Agent for free. It's a confidential tool that helps innovators synthesize their inventions and instantly see patentable insight. Visit landfallip.com to learn more. That's landfallip.com Today's episode is brought to you by robots and pencils. When competitive advantage lasts mere moments, speed to value wins the AI race. While big consultancies bury progress under layers of process, robots and pencils builds impact at AI speed. They partner with clients to enhance human potential through AI modernizing apps, strengthening data pipelines and accelerating cloud transformation. With AWS certified teams across us, Canada, Europe and Latin America, clients get local expertise and global scale. And with a laser focus on real outcomes, their solutions help organizers work smarter and serve customers better. They're your nimble, high service alternative to big integrators. Turn your AI vision into value fast. Stay ahead with a partner built for progress. Partner with robots and pencils at robotsandpencils.com aidaily Brief. Now moving on from the big competitive and AI race dynamics just to the new capability set from this week One of the things that was extremely clear interacting with nanobanana Pro is is that it really feels like we've barely scratched the surface on what native multimodal AI can be. You know, one of the interesting commentaries following the launch of GPT5 was from Altman once again basically saying that in some ways there was only so much more performance that they could eke out from LLM and text based chat, but there was still a ton of opportunity across other AI modalities. It really feels like to me that the way that the Gemini 3 suite, including Nano Banana Pro, integrates the native reasoning of Gemini 3 + the image generation capabilities of NB Pro shows a glimpse of what a native multimodal future can do. When you ask NANOBANANA3 to create an infographic, it's not just that it does a good job on the visuals, or even that it does a good job rendering the text, although it does. It's that it's able to understand the source material and do the work to consolidate and compress the information, making judgments about what it should and shouldn't share. And all of that informs the ultimate output, which is the visual infographic. Now that's just one tiny use that shows the different type of capabilities that will exist in a natively multimodal regime. And that's the sort of thing we have to look forward to. Speaking of which, turns out that reasoning plus text and images opens just an absolutely insane number of use cases. If you have not yet listened to Friday's episode about the 25 new things you can do with Nanobanana Pro and Image generation that you couldn't just a little while ago, you really should go check it out just for the sake of all of the inspiration that you're going to get. I have this concept of utility score, which is basically a way of looking at new models in terms of not what they hit on the standard academic and industry benchmarks but instead how many new things we can do with them that weren't possible before and this week just smashed open a lot of those barriers. The way that we share visual information is going to change. The way that we study and educate is going to change. I just experimented with doing infographics as a standard part of releasing my episodes. It very much feels like we are at the beginning of a new journey when it comes to discovering the use cases that these new capabilities open up. Now, moving away from nanobanana for a minute, it is also clear after this week that while we might be distracted a little bit with these flashy new visual capabilities, coding is and remains a key battleground for especially professional AI. Now, part of that is that one area where Gemini 3 wasn't completely dominant instantly on the benchmarks was around coding. In fact, Gemini 3 Pro was behind Cloudsonnet 4.5 and GPT 5.1 when it came to Sweep Verified. Not far behind, but a little more than that. OpenAI's big response to Gemini 3 was actually not even 5.1Pro, which only got a tweet announcement. It was instead this new coding model, Codex Max. When Sean Wang, the host of Latent Space and the curator of the AI Engineer Summit, better known as swix, announced that he was moving to cognition, part of the reason that he gave is that he thinks that code AGI is about 80% of the rest of AGI and so why not work on that now? And you get the sense that a lot of the labs agree with him, at least in terms of the significance of that particular area. Now of course it is notable that the outputs of coding may also get a benefit from other parts of the developments. This week I'm thinking in particular about vibe coding platform Replit's new design mode, which is powered by Gemini 3, which significantly ups the level of visual quality and design of Vibe coded projects. And so all of these things are to some extent connected. Still, I think that while we didn't anticipate just how central to the entire 2025 AI story coding was going to be, I anticipate that it will be every bit as Central in 2026, but if not this time, unexpectedly. Lastly, today we have to talk about the markets. There was a brief moment, long enough for me to get a part of an episode out where it looked like the Nvidia blowout earnings report and projections had temporarily at least popped the AI bubble bubble. Jensen Huang reframed the whole AI bubble conversation, talking about the three paradigm shifts happening simultaneously and initially markets bought it they surged. The next day, however, Nvidia was down again and it's very clear that right now the market is just not comfortable with where it is now. I tend to think that there's a lot more going on than just in AI. I think that AI specific factors are part of the story. I think that the 1.4 trillion of deals that OpenAI announced was just a little bit too much for the markets to digest comfortably and actually increase the overall level of skepticism. But I also think that the markets have pinned their entire hopes and dreams on AI for the last three years, ever since the cutting cycle began. And there are just too many other things that aren't going all that well outside of AI that are weighing on the whole we don't have any real economic data for the last couple of months because of the shutdown. We have an extremely volatile political economic environment. We don't have any clarity around what the Fed is going to do when it comes to monetary policy. At the time I was prepping this episode, the Fear and Greed Index was down at something like eight. Just incredibly fearful. And so, like I said, while I do think certain parts of what's going on are AI specific, I also think that there is a much bigger picture that for the first time in a very long time, even AI isn't able to sweep under the rug. Still, while that's the case, I do notice a bit of an increasing sophistication around the market discourse on AI in ways that I think could be really positive over time. Gavin Baker, who is at Gavin S. Baker on Twitter Xai, wrote a great piece that's pinned to the top of his profile called some thoughts on AI where he argued that Gemini 3 was the most important AI data point since the release of 01 because of the way that it showed scaling laws for pre training are intact Now. His piece goes into a lot of the economics around chips, residual value in GPUs, ROI of AI and comes to the conclusion all of this suggests we are still very early in AI. I understand the OpenAI jitters. The 1 trillion of unfunded spending commitments cast unfortunate doubt on the powerful underlying reality of AI. Today. OpenAI has lost share and is decisively behind to other companies from a model quality perspective for the first time. However, as Gavin points out, the Internet trade survived the demise of Yahoo, MySpace and AOL. I don't think OpenAI losing share to Google and or others will materially impact overall token demand, and token demand as a function of customer ROI is what ultimately matters. The share of those tokens will matter to the relative market caps of Google, OpenAI, XAI and Anthropic. But overall token demand is what will drive all of the suppliers. Ultimately, he concluded tonight will be just one data point in what I think will be a decade of steady AI progress. And on that note the thing that I want to close with bringing it back to us personally is that if there is one key thing to take away from this week is that more so than basically any other week in 2025 you can do way more right now with AI than you could a week ago. This has been by a mile the most spectacular capability increase period we have had for an extraordinarily long time. We are barely scratching the surface on what we can do with all these new tools and toys and I cannot wait to get back to trying them out. So with that said I will wrap it here. Appreciate you guys listening or watching as always. Until next time peace.