The AI Daily Brief: Artificial Intelligence News and Analysis - 15 Business Model Questions for OpenAI and Anthropic

Episode Date: October 17, 2025

This episode explores the massive revenue growth of OpenAI and Anthropic and what it means for their business models. With Anthropic hitting a $7 billion revenue run rate and OpenAI reaching $13 billion , the discussion weighs their strategic futures, including the push into enterprise versus consumer markets and the potential for new revenue streams like advertising. In the headlines, NLW covers Claude's important new 'Skills' feature, Microsoft's AI PC push, and Spotify's new deal with music labels.Brought to you by:Is your enterprise ready for the future of agentic AI?⁠⁠⁠⁠⁠⁠⁠Visit AGNTCY.org⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Visit Outshift Internet of Agents⁠⁠⁠⁠⁠⁠⁠Google Gemini - Try NotebookLM today https://notebooklm.google.com/KPMG – Discover how AI is transforming possibility into reality. Tune into the new KPMG 'You Can with AI' podcast and unlock insights that will inform smarter decisions inside your enterprise. Listen now and start shaping your future with every episode. ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.kpmg.us/AIpodcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Blitzy.com - Go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://blitzy.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to build enterprise software in days, not months Robots & Pencils - Cloud-native AI solutions that power results ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://robotsandpencils.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Agent Readiness Audit from Superintelligent - Go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://besuper.ai/ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠to request your company's agent readiness score.The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: https://pod.link/1680633614Interested in sponsoring the show? [e-mail]

This podcast is supported by Google. Hey folks, Steven Johnson here, co-founder of NotebookLM. As an author, I've always been obsessed with how software could help organize ideas and make connections. So we built NotebookLM as an AI first tool for anyone trying to make sense of complex information.

Upload your documents and NotebookLM instantly becomes your personal expert, uncovering insights and helping you brainstorm. Try it at notebooklm.gov.com. Today on the AI Daily Brief, some monster revenue numbers bring up a slew of questions on the business model for Open AI and Anthropic. Before that in the headlines, why Claude's new skills feature is potentially a really big deal. 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.

Firstly, thank you to today's sponsors, Gemini, Notion, Blitzy, super intelligent, and robots and pencils. To get an ad-free version of the show, go to Patreon. com slash AI Daily Brief, or you can subscribe on Apple Podcasts, and to find out about sponsorship opportunities, or pretty much anything else, speaking, job opportunities, etc., visit the show at AIdailybrief.aily brief.com. Welcome back to another AI Daily Brief Headlines edition, all the daily AI news you need in around five minutes, and we have a jam-packed edition today. We're kicking off today with a story that I think I will probably try to do a more Operators cut style episode as people dig in and figure out how to use these tools in the coming weeks, but for now, it's rolled out a new

feature called Skills, which provides agents with instructions, scripts, and resources to help them with specific tasks. Users can fill a folder with these skills that might cover things like brand guidelines or instructions on carrying out a task in Excel. The feature also allows users to provide executable code for situations where traditional programming is more reliable. Claude agents can then draw from these skills when they become relevant to the task at hand. Essentially, skills are little barrels or buckets of context that Claude can draw on when it makes sense. They're in a standard format that can be used across clod apps, cloud code, and the API, meaning you only have to build them once. Said Anthropic staff from

Maheshmerag, skills are based on our belief and vision that as model intelligence continues to improve, will continue moving towards general purpose agents that often have access to their own file system and computing environment. The agent is initially made aware only of the names and descriptions of each available skill and can choose to load more information about a particular skill when relevant to the task at hand. Now, part of the benefit here is that this makes the method token efficient. Clawed agents can initially use very basic tools to figure out which skills they need for a given task and only spend significant tokens once it knows which ones to load.

They also function sort of like custom agentic scaffolding but in a much more modular and user-friendly package. A user doesn't need to know any programming language to create a custom skill that's fit for their purpose, which of course dramatically lowers the barrier to entry for advanced agent design. You can also prompt Claude to design its own skills with the example that they give, saying, Help Me Create an Image Editor Skill. Claude can also help then refine human design skills or monitor common failure points and then

build skills to mitigate them. Basically, Claude can be leveraged to collaborate on its own agentic design. Skills are also stackable. So by way of example, Anthropic discussed an agendic workflow for building a quarterly investor deck, where the agent would be able to tap into the company's brand guideline skill, a financial reporting skill, and a presentation formatting skill, coordinating all three without the need for manual intervention. People very quickly picked up that this is sneakily a big deal. Daniel Meisler wrote, something I want to stress today. MCP changed everything, but not because of a model improvement.

And today, skills change everything, but not because of a model improvement. AI systems are the thing to watch, not just the intelligence of the models. People are useful in jobs because they connect dots and can do many different things. That's what Anthropic has been doing here, building a unified system that connects dots. Simon Willison discussed in his blog how skills are awesome and maybe a bigger deal even than MCP, which is of course

some bold words. First, he described what skills are, he said that conceptually they're extremely simple, they're a markdown file telling a model how to do something, optionally accompanied by extra documents and pre-written scripts that the model can use to help it. However, he writes, there's one extra detail that makes us a feature, not just a bunch of files on disk.

At the start of a session, Claude's various harnesses can scan all available skill files and read a short explanation for each one. This is very token efficient. Each skill only takes up a few dozen extra tokens, with the full details only loaded in should the user request a task that the skill can help solve. He discussed how he would build a data journalism agent using the feature. The skills that he would build were things like how to access and parse census data, how to load data from SQL and duct DB with associated Python code provided,

how to publish data online, and how to figure out an interesting story to tell based on the data. Simon concluded, The core simplicity of the skills design is why I'm so excited about it. Skills are marked down with a tiny bit of YAML metadata, and some optional scripts in whatever you can make executable in the environment. They feel a lot closer to the spirit of LLMs, throw in some text, and let the model figure it out. They outsource the hard parts to the LLM harness and the associated computer environment.

Given everything we have learned about LLM's ability to run tools over the last couple of years. I think that's a very sensible strategy. Put differently, I think skills are in some ways a different user experience pattern for getting at agent creation. That's really what this is about. On the one hand, you've got the N8N-N-style agent workflow builder, but this is basically where you articulate component parts and then can use natural language to help the LLM itself figure out which of those parts it needs to put together. Again, in Simon's example, he's got a skill for parsing census data, a skill for loading data, a skill for publishing data online, and a skill for figuring out an interesting story. Instead of having to put them together in a step-by-step

workflow schematic, he can just make sure that each individual piece has all of the context it needs, and then the LLM can figure out how to put it together, which strikes me if it works as a much more intuitive way to organize this than some of those other interfaces. There's going to be a ton of examples, I think, coming up, and like I said, I'm planning on maybe an operator's cut style episode where we get into some ways to actually use Claude Skills. But for now, we've got a lot to cover, so let's move on to Microsoft, who wants to make every PC an AI PC with a big new update to Windows 11. Now, on the surface, this is an update about bringing copilot to all users and making it more central to the Windows experience. Users can now summon the AI assistant by saying, hey, copilot. They're

also rolling out copilot vision, which allows the AI model to see what's happening on the desktop and use it as input. In addition, agentic features will be introduced as early previews. Those agendic features will allow copilot to tap into data from emails, calendars, and the office suite, as well as taking actions in the file system itself. The biggest difference from previous AI releases is that none of these features will be restricted to copilot plus hardware. They will just be a default part of the Windows 11 experience for all users. Executive VP and Consumer Chief Marketing Officer Yusef Medi explained, we think we're on the cusp of the next evolution, where AI happens not just in a chatbot and gets naturally integrated into the hundreds of millions

of experiences that people use every day. The vision that we have, is, let's rewrite the entire operating system around AI and build essentially what becomes, truly the AI PC. The rewrite is built around two main features, Agentix and Voice. Medi said, you should be able to talk to your PC, have it understand you, and then be able to have magic happen from that. With your permission, we want people to be able to share with their AI on Windows what they're doing and what they're seeing. The PC should be able to act on your behalf. He added, In our minds, voice will now become the third input mechanism to use with your PC. It doesn't replace the keyboard and mouse necessarily, but it's an added thing and it will be pretty

profound in a new way to do it. They also have a new feature coming called copilot actions, which is sort of similar to OpenAI's operator or Google's new Gemini Enterprise. The feature spins up a new window where the users can give directions and watch the agent complete tasks using local files. They can either monitor the agent and take over at any time or click away to a different window and let the agent run in the background. Now, as I've said before, Despite what seem like some stumbles and missteps, Microsoft has an incredible distribution network and serious benefits when it comes to consumer installs. And this is a great example of how owning the entire end-end experience could be a game changer when it comes to how much value

you can unlock for a particular user. Moving over to IPLand, Spotify has reached a deal with the major music labels on how AI will intersect with the music industry. Spotify will collaborate with Sony, Universal, Warner, Merlin, and Believe, to quote, develop responsible AI products, that empower the artists and songwriters they represent and connect them with the fans who support them. In a press release, Spotify wrote, Some voices in the tech industry believe copyright should be abolished. We don't.

Musicians' rights matter. Copyright is essential. If the music industry doesn't lead in this moment, AI-powered innovation will happen elsewhere, without rights, consent, or compensation. Now, the announcement didn't discuss any specific products, but it did reinforce that the introduction of AI features would fundamentally be the choice of rights holders and artists.

They're also aiming to build AI products that create wholly new revenue streams and want to ensure that rights holders are properly compensated and credited appropriately. A lot of the coverage was extremely skeptical. Spotify has recently been criticized for allowing low-effort AI tracks to proliferate, leading to a purge of 75 million AI-generated tracks in September, but others are willing to give the benefit of the doubt. Ed Newton-Rex, the CEO of Copyright Advocacy Group, fairly trained, posted. In my opinion, it's a good thing that Spotify is working on AI music tools with the major labels. Lots of the AI industry is exploitative.

built on people's work without permission served up to users who get no say in the matter. This sounds like it will be different. AI features built fairly with artist's permission, presented to fans as a voluntary add-on, rather than an inescapable funnel of AI slop. To be clear, Spotify's general stance on AI music is not good. They seem happy to allow AI slop on the platform without labeling it, even featuring it in recommended playlists. This needs to change. But credit where it's due. Licensing music for training instead of taking it without permission is very welcome. The devil will be in the details, but in tone at least this is a positive development.

Now, that might not sound all that positive to you, but given how absolutely contentious the relationship is between the AI and anti-AI crowd, that is not just a fig leaf, that's a fig tree. Whether Oracle can actually pull off that line remains to be seen, but I think that they are in a good position to give it a shot. A couple quick product updates. General Purpose Agent Manus has been updated for version 1.5. The team wrote faster, better quality results, unlimited context, build full-stack web apps

with real AI features, backends, user logins, custom domains, and analytics. Run your biz, start your side hustle, or just have fun. If you can dream it, Manus can build it. Manus claimed that tasks are now four times faster on average, and the performance boost should allow tasks that once failed to be completed successfully. Manus's internal benchmark showed a 15% improvement in task quality and a 6% increase in user satisfaction. The big new feature is the ability to build web apps and to end without leaving Manus,

making the agent more useful as a vibe coding platform. Kind of seems like ultimately everything, even the general purpose agents, are a vibe coding platform in the end. Another small but potentially powerful feature announcement, Agent Orchestration startup Lindy has announced an AI chief marketing officer for your company. CEO Flo Crivello wrote, announcing Lindy AICMO, a team of agents running entire marketing workflows end-to-end, handling market research, analysis, and creative at scale, so you can start thousands of ad experiments in minutes. As part of this, we're launching new integrations with SORA 2, V03.1, Nano Banana, and GBT, ImageGen. Over the last few years, agents have grown tremendously in their scope and autonomy,

going from handling simple tasks to assuming entire roles. This will be the first time that we see them take a chunk out of an entire org. I'm considering trying this out as CMO of AIDB, given that as I've mentioned, I'm hiring for some growth roles. If that's an experiment that you would be interested in following along with, let me know and we'll see what can happen. Lastly, I am continuing to do our ROI Corner, ROI shoutouts. RUI is going to be a major theme for next year. And so when we see companies reporting actual ROI from AI, you are going to get it here. The latest comes from Alibaba, who have announced that they've reached break-even on AI in their e-commerce business. In February of this year, the Chinese tech giant announced they would spend

$53 billion on AI over the next three years. They've since rolled out a string of features, including personalized AI search and virtual clothing trions. Now a company official has announced that preliminary testing of their AI features has shown consistent results, including a 12% increase in return on advertising spend. VP Kifu Zhang said it's very rare to see double-digit changes. Zhang predicted that AI would have a, quote, very significant positive impact on sales during this year's singles day shopping period, which centers around November 11th. Meanwhile, I would anticipate that in the U.S., we're going to see a bit of an AI-focused Black Friday as well, but it's not even Halloween yet. We still have a little time before that. And so with that, we will wrap today's

headlines. Next up, the main episode. Small, nimble teams beat bloated consulting every time. Robots and Pencils partners with organizations on intelligent, cloud-native systems powered by AI. They cover human needs, design AI solutions, and cut-through complexity to deliver meaningful impact without the layers of bureaucracy. As an AWS-certified partner, Robots and Pencils combines the reach of a large firm with the focus of a trusted partner. With teams across the U.S., Canada, Europe, and Latin America, clients gain local expertise and global scale. As AI evolves, they ensure you keep peace with change. And that means faster results, measurable. outcomes and a partnership built to last. The right partner makes progress inevitable.

Partner with robots and pencils at robots and pencils.com slash AI Daily Brief. Chatbots are great, but they can only take you so far. I've recently been testing Notion's new AI agents, and they are a very different type of experience. These are agents that actually complete entire workflows for you in your style, and best of all, they work in a channel that you already know and love because they are purpose-built Notion super users. Notion's new AI agents completely expands the range of what Notion can do. It can now build documents from your entire company's knowledge base, organize scattered information into organized reports,

basically do tasks that used to take days and get them complete in minutes. These agents don't just help with work, they finish it. Getting started with building on Notion is easier than ever. Notion agents are now your very own super user to help you onboard in minutes. Your AI teammates are ready to work. Try Notion AI for free at the link in our show notes. What if AI wasn't just a buzzword, but a business imperative? On You Can with AI, we take you inside the boardrooms and strategy sessions of the world's most forward-thinking enterprises.

Hosted by me, Nathaniel Wittamore, and powered by KPMG, the seven-part series delivers real-world insights from leaders who are scaling AI with purpose, from aligning culture and leadership to building trust, data readiness, and deploying AI agents. Whether you're a C-suite executive, strategist, or innovator, this podcast is your front row seat to the future of Enterprise AI. So go check it out at www.kpmg.org.us slash AI podcasts or search you can with AI on Spotify, Apple Podcasts, or wherever you get your podcasts. Today's episode is brought to you by Super Intelligent. Now, for those of you who don't know, who are new here maybe, Super Intelligent is actually my company. We started it because every

single company we talk to, all the enterprises out there, are trying to figure out what AI can do for them, but most of the advice is super generic, not specific to your company. So what we do is we map your AI and agent opportunities by deploying voice agents to interview your teams about how work works now and how your people would like it to work in the future. The result is an AI action map with high potential ROI use cases and specific change management needs, basically everything you need to go actually deliver AI value. Go to Bsuper.a.ai to learn more. Welcome back to the AI Daily Brief. At the core of all of the conversations around whether or not we are in an AI bubble is the question of the growth potential of the core model provider companies.

Basically, we have a situation where there's this set of deals that are perceived as increasingly circular that are all based on getting more compute and more electricity to power that compute, more data centers to deliver that compute, et cetera. But all of that is based on the assumption that that compute will turn into AI products that have enough to make. to generate enough revenue to make all of that infrastructure buildout makes sense. So for that reason, people are paying extremely close attention to indications of how these businesses are growing. And the short answer seems to be like gangbusters. So we got two interesting reports this week, both of which dealt to some extent with revenue from these companies.

The first was about Anthropic, and it showed just what an absolute tear this company has been on this year. Now, Anthropics started the year at around $1 billion. annualized revenue. That's where they were in the December-January time frame. Over the summer, they nudged up to $5 billion in annualized revenue, and according to Reuters, they are now running at a $7 billion revenue run rate and are on track to meet a goal of $9 billion by the end of the year. 900-X growth from a billion dollar base in a single year is absolutely insane. And while they don't think that they're going to continue that unbelievably breakneck pace into 2026, they still do think that they can reach between 20 billion and 26 billion in annualized revenue by the end of next

year. Okay, so that's the anthropic story. Open AI, meanwhile, began the year at around 5.5 billion, and have now grown apparently to around 13 billion. Now, in any normal world, zero to 13 billion in three years after the release of their core product would be seen as absolutely insane growth, and it is. The challenge is that they are projecting 100 billion in annual revenue by 2028. The Financial Times wrote a piece about the big obligations that OpenAI is putting on its back called OpenAI makes five-year business plan to meet $1 trillion spending pledges. This, of course, they're referring to all these deals with Invidia, Oracle, etc. Now, it's very clear that Open AI isn't planning on being able to finance all of this just with

revenue. However, as I said, they are projecting $100 billion in three years, meaning they are going to need to see a big uptick in revenue from where they are. And alongside the financial Times article, we also got some reported information around the state of the business. 13 billion in revenue includes about 70% of that from consumer subscriptions and around 30% of that from the API. Of their 800 million users, about 5% are paying monthly or 40 million. They're also at a $20 billion burn rate. So let's talk about some of the interesting questions that I think this brings up that are more slanted towards the strategy of these companies going forward. So one question is, does the radical breakout success of Anthropic push

OpenAI to be more aggressive about enterprise. In that Reuters piece, we also learned that 80% of Anthropics revenue comes from the enterprise. They have more than 300,000 business and enterprise customers. They've recently announced big deals like the one with Deloitte, where they have hundreds of thousands of users coming from a single company. And given their 9x growth, the fact that they have gone from 1.20th or less of OpenAI's revenue to more than half of open AI's revenue in just 10 months has got to be pushing the question of whether there should be more emphasis on the enterprise from OpenAI. Now, of course, it's not like OpenAI has no enterprise focus. The company has even recently added a forward-deployed engineer team that will go in and

actually help companies build out solutions if apparently they are a $10 million or more customer. And interestingly, at Dev Day, Altman and Brockman did speak a little bit about enterprise. Brockman talked about how there were a lot of really boring enterprise problems that were some of the juiciest things to solve. And Altman talked about how they didn't just want FTEs to be bungling around, but now they feel like they really have a good idea of how to help enterprises, and so they want to scale that. All of that suggests to me that we might see at least a bit more enterprise focus heading into 2026. Now, of course, one of the interesting questions, which has a big stake in this strategy as well, is how much of this enterprise success is strictly anchored in Anthropics' coding prowess.

For most of this year, really up until GPT5, Anthropic were the preferred coding models by far and away, with Gemini models a distant second, and open AI models not really in the conversation. We've covered a lot about how that shifted, and it was very clear that a huge goal of GPT5, if not the primary goal of GPT5, was to redress this balance. And I wondered to what extent OpenAI sees coding as the Trojan horse for absolutely everything in Enterprise and as the thing they need to focus on, even if their broader goal, is more general enterprise revenue as well. Still, I don't think it's a foregone conclusion that OpenAI only plays to double down

on Enterprise. The numbers that Anthropic is putting up certainly suggest how valuable that segment is, but at the same time, OpenAI really does have a unique position vis-a-vis consumer. For a huge number of people, AI and ChatGPT are completely inextricable from one another. In fact, ChatGPT is the term more than AI that they know. Aside from a few dips and blips, they've been the number one app for a huge part of their life since ChatGPT was launched, and every week that goes on, chat Chbett gets more and more deeply woven into the consumer experience. Now, one of the questions for a lot of people is how much more there is to be gained from those 800 million users.

Sparrow, for example, tweeted, is it just me or is 40 million paying chat GPT users kind of low? Spotify, for example, has 276 million paid subscribers. Just kind of surprised that there are 760 million people who don't feel like they derive an additional $20 of value from a more capable model. This to me, I think, is a really important question. It certainly feels like there is a lot more to be gained from these users. And my guess is that OpenAI is betting that the more valuable that these models get over time, the higher the percentage of users they'll be able to unlock and convert to a higher tier.

But it also seems like they're not taking that for granted. And so another question that comes up is, does this make ads inevitable? My read having watched all of these discussions is that OpenAI really doesn't want to have to do ads, at least not in the traditional way. I think that they genuinely don't really want to be strictly speaking in the attention business in the way that meta or TikTok or Instagram are. But I also think that they feel like it is to some extent inevitable. and a huge source of revenue. And certainly, if you're trying to go from 13 billion where we are at the end of 2025 to 100 billion by the end of 2028, it seems like there are going to have to be new revenue streams unlocked that aren't just converting more paid subscribers. Certainly, I think that that brings up the

question of how much revenue is possible from ads and what they need it to do as part of the overall mix if they were to go into this area. Now, one thing that hasn't been discussed as much is that it is possible that OpenAI finds some slight variation on the traditional ad unit experience that better befits the user experience patterns of interacting with chat chagipt, and that make it less annoying and more palatable for users than, for example, big chunky display ads. Indeed, it feels like that's kind of what they're thinking about with their checkout with chat chit business and in general taking a cut of referrals from the part of the chat chit experience that's all about discovery. This definitely feels like a thing that they're going to do

and an area where a version of advertising feels less disruptive of the user experience and more potentially additive or at least integrated with the user experience. And I wouldn't be surprised if that's the sort of template that we see when it comes to more traditional advertising-style business models for OpenAI is trying to find things that are more subtle and more integrated as ways to capture additional incremental value from each average user. I think one of the big questions for me is whether some amount of this solves itself as we just better understand what use cases actually drive the most value. In other words, I think that probably a lot of the users of chat GPT are radically under-maximizing their use of it. And the more time that goes on, the more

people around them will have figured out how to use it for more value, and the more likely they are to uncover some use case that's worth the 20 bucks a month or whatever the price is for them to become a paid user. One of the interesting ways to look at the study that OpenAI did about how people use chat GTT is through this lens of trying to understand which use cases actually drive the most value, again, with an eye to are those possibly the use cases that could be better monetized. There's also the interesting question, I think, of whether subscriptions in general are the right model and whether these subscription prices are the right prices for those subscriptions. To some extent, we're kind of all just living in the environment that Altman and Open

AI plucked out of the air when it came to pricing. They decided 20 bucks a month for consumer and 30 bucks a month for enterprise users sort of made sense. And then when they added a $200 a month premium subscription, everyone anchored to that. In other words, there hasn't been a ton of price discovery around whether these are exactly the right prices. And I think that there's probably some room for experimentation there as well. Now, on the enterprise side, one of the major questions, I think, is to what extent companies are going to prioritize just state-of-the-art models versus there's going to be more room for cheaper models for even production-grade use cases. My instinct is that we are right on the cusp of a threshold where big chunks of high-value use cases are completely viable

with not state-of-the-art models, but with the newer, more efficient models like Claude's Haiku 4.5. I also think with Agentic systems, they're going to start to get smart enough that they know which models they need to use for different tasks, and they're not always going to have to call on the most expensive models. We even got a little glimpse of how this type of organization might work when Claude Skills was launched, where one of the things that people are excited about for skills, is that they don't need a super sophisticated model to initially see a user's request and figure out which skills to draw from, making the whole experience more efficient for the user. Part of why I'm bullish on the enterprise use case, even for OpenAI that has such a distinct

advantage in consumer, is that I just think that especially as the price equation gets figured it out, we're going to see some amount of augmentation or automation across basically every core business workflow, and that is just a ton of tokens that can be consumed for revenue. Now, I should note it here at this point that I'm just talking about OpenAI and Anthropic because those are the ones we got revenue numbers from this week. Obviously, GROC, Gemini, and Microsoft co-pilot are all in this conversation as well, and what they do could impact these questions. For example, one of the good reasons for Open AI not to focus on enterprise so much is if they think that space is just going to be too out-competed. One sort of interesting question that's more about how much these companies become a black hole for revenue in the sector in general is whether the foundation model companies like Anthropic and OpenA. likely to siphon off revenue from the vertical apps or the other way around. Related to that,

are we likely to see more verticalized products from OpenAI and Anthropic. We've seen the first indications of them dancing with that. Anthropic has released some industry-specific tools, but I think this is one of the big important questions for how this all shakes out. There are right now a lot of AI companies out there who have raised to nine figures of revenue coming up even on a billion, and whether ultimately the foundation model companies are a great big sucking sound that pulls all that in, or whether those room for those companies as well, could also shape how this market plays out. I think related to that is the question of whether we're going to see these companies start to acquire their way to additional revenue. The information this week also posted an article

about how Anthropic is planning on doing more acquisitions. They wrote Anthropic told investment bankers in recent weeks that it's getting ready to move off the sidelines and do more acquisitions. It is apparently considering M&A targets that could be good for its tech or for its end products. Still, at least for now, it does seem like the acquisitions that they're interested and are a little bit more on the technical side and less on the product side, but there's no saying that that won't change. Ultimately, the two big questions are these. The first is, can any company grow this fast? Epic AI research dug into this and pointed out that we are in extremely uncharted territory. They wrote, Open AI's revenue growth has been extremely impressive, from less than

a billion to over 10 billion in only three years. We found four other U.S. companies in the past 50 years that have done this, but of those, they point out, only Google went on to reach above 100 billion in revenue. Trying to find other companies that went from 10 billion to 100 billion in revenue in under a decade, they found seven. Google, Walmart, Apple, Amazon, Tesla, meta, and Nvidia, but none of them even did it in six years, let alone three. Now again, we are in uncharted territory, but it really would represent the fastest growth of anything that we've ever seen. Which brings us to our second question, what is sufficient growth? And by that I mean sufficient growth to make the market still have confidence in all of these deals in the big

infrastructure buildout. This is, of course, a completely subjective question that could change wildly based on market conditions that have nothing to do with AI. Sufficient in this case is a barometer of investor confidence in the future, not any sort of harder objective function. And so to some extent, if you want to keep track of where we are in the bubble cycle, I think the key thing to look at is how the growth numbers that we're getting from these companies compared to what is the inevitably moving target of sufficient growth. Look, ultimately we are in completely uncharted territory, and the future is being written live as we watch.

It is going to be a hell of a couple years, so buckle in, listen to AIDB, and get ready for a whirlwind. That's going to do it for today's AID Daily Brief. Appreciate you listening or watching, as always. Until next time, peace.