Action is happening up-and-down the LLM stack: Nvidia is making deals with Intel, OpenAI is making deals with Oracle, and Nvidia and OpenAI are making deals with each other. Nine years after Nvidia CEO Jensen Huang hand-delivered the first Nvidia DGX-1 AI computer to OpenAI, the chip giant is investing up to $100 billion in the AI lab, which OpenAI will, of course, spend on Nvidia AI systems.
This ouroboros of a deal certainly does feel a bit frothy, but there is a certain logic to it: Nvidia is uniquely dominant in AI thanks to the company’s multi-year investment in not just superior chips but also an entire ecosystem from networking to software, and has the cash flow and stock price befitting its position in the AI value chain. Doing a deal like this at this point in time not only secures the company’s largest customer — and rumored ASIC maker — but also gives Nvidia equity upside beyond the number of chips it can manufacture. More broadly, lots of public investors would like the chance to invest in OpenAI; I don’t think Nvidia’s public market investors are bothered to have now acquired that stake indirectly.
The interconnectedness of these investments reflects the interconnectedness of the OpenAI and Nvidia stories in particular: Huang may have delivered OpenAI their first AI computer, but it was OpenAI that delivered Nvidia the catalyst for becoming the most valuable company in the world, with the November 2022 launch of ChatGPT. Ever since, the assumption of many in tech has been that the consumer market in particular has been OpenAI’s to lose, or perhaps more accurately, monetize; no company has ever grown faster in terms of users and revenue, and that’s before they had an advertising model!
And beyond the numbers, have you used ChatGPT? It’s so useful. You can look up information, or format text, and best of all you can code! Of course there are other models like Anthropic’s Claude, which has excelled at coding in particular, but surely the sheer usefulness makes ultimate success inevitable!
A Brief History of Social Media
If a lot of those takes sound familiar, it’s because I’ve made some version of most of them; I also, perhaps relatedly, took to Twitter like a fish to water. Just imagine, an app that was the nearly perfect mixture of content I was interested in and people I wanted to hear from, and interact with. Best of all it was text: the efficiency of information acquisition was unmatched, and it was just as easy to say my piece.
It took me much longer to warm up to Facebook, and, frankly, I never was much of a user; I’ve never been one to image dump episodes of my life, nor have I had much inclination to wade through others’. I wasn’t interested in party photos; I lusted after ideas and arguments, and Twitter — a view shared by much of both tech and media — was much more up my alley.
Despite that personal predilection, however, and perhaps because of my background in small town Wisconsin and subsequently living abroad, I retained a strong sense of the importance of Facebook. Sure, the people who I was most interested in hearing from and interacting with may have been the types to leave their friends and family for the big city, but for most people, friends and family were the entire point of life generally, and by extension, social media specifically.
To that end, I was convinced from the beginning that Facebook was going to be a huge deal, and argued so multiple times on Stratechery; social media was ultimately a matter of network effects and scale, and Facebook was clearly on the path to domination, even as much of the Twitterati were convinced the company was the next MySpace. I was similarly bullish about Instagram: no, I wasn’t one to post a lot of personal pictures, but while I personally loved text, most people liked photos.
What people really liked most of all, however — and not even Facebook saw this coming — was video. TikTok grew into a behemoth with the insight that social media was only ever a stepping stone to personal entertainment, of which video was the pinnacle. There were no network effects of the sort that everyone — including regulators — assumed would lead to eternal Facebook dominance; rather, TikTok realized that Paul Krugman’s infamous dismissal of the Internet actually was somewhat right: most people actually don’t have anything to say that is particularly compelling, which means that limiting the content you see to your social network dramatically decreases the possibility you’ll be entertained every time you open your social networking app. TikTok dispensed with this artificial limitation, simply showing you compelling videos period, no matter where they came from.
The Giant in Plain Sight
Of course TikTok wasn’t the first company to figure this out: YouTube was the first video platform, and from the beginning focused on building an algorithm that focused more on giving you videos you were interested in than in showing you what you claimed to want to see.
YouTube, however, was and probably always has been my biggest blind spot: I’m just not a big video watcher in general, and YouTube seemed like more work than short-form video, which married the most compelling medium with the most addictive delivery method — the feed. Sure, YouTube was a great acquisition for Google — certainly in line with the charge to “organize the world’s information and make it universally accessible and useful” — but I — and Google’s moneymaker, Search — was much more interested in text, and pictures if I must.
The truth, however, is that YouTube has long been the giant hiding in plain sight: the service is the number one streaming service in the living room — bigger than Netflix — and that’s the company’s 3rd screen after mobile and the PC, where it has no peer. More than that, YouTube is not just the center of culture, but the nurturer of it: the company just announced that it has paid out more than $100 billion to creators over the last four years; given that many creators earn more from brand deals than they do from YouTube ads, that actually understates the size of the YouTube economy. Yes, TikTok is a big deal, but TikTok stars hope to make it on YouTube, where they can actually make a living.
And yet, YouTube sometimes seems like an afterthought, at least to people like me and others immersed in the text-based Internet. Last week I was in New York for YouTube’s annual “Made on YouTube” event, but the night before I couldn’t remember the name; I turned to Google, natch, and couldn’t figure it out. The reason is that talk about YouTube mostly happens on YouTube; I, and Google itself, still live in a text-based world.
That is the world that was rocked by ChatGPT, especially Google. The company’s February 2023 introduction of Bard in Paris remains one of the most surreal keynotes I’ve ever watched: most of the content was rehashed, the presenters talked as if they were seeing their slides for the first time, and one of the demos of a phone-based feature neglected to remember to have a phone on hand. This was a company facing a frontal assault on their most obvious and profitable area of dominance — text-based information retrieval — and they were completely flat-footed.
Google has, in the intervening years, made tremendous strides to come back, including dumping the Bard name in favor of Gemini, itself based on vastly improved underlying models. I’m also impressed by how the company has incorporated AI into search; not only are AI Overviews generally useful, they’re also incredibly fast, and as a bonus have the links I sometimes prefer already at hand. Ironically, however, you could make the case that the biggest impact LLMs have had on Search is giving a federal judge an excuse to let Google continue paying its biggest would-be competitors (like Apple) to simply offer their customers Google instead. The biggest reason to be skeptical of the company’s fortunes in AI is that they had the most to lose; the company is doing an excellent job of minimizing the losses.
What I would submit, however, is that Google’s most important and most compelling AI announcements actually don’t have anything to do with Search, at least not yet. These announcements start, as you might expect, with Google’s Deep Mind Research Lab; where they hit the real world, however, is on YouTube — and that, like the user-generated streaming service, is a really big deal.
The DeepMind-to-YouTube Pipeline
A perfect example of the DeepMind-to-YouTube pipeline was last week’s announcement of Veo 3-based features for making YouTube Shorts. From the company’s blog post:
We’ve partnered with Google DeepMind to bring a custom version of their most powerful video generation model, Veo 3, to YouTube. Veo 3 Fast is designed to work seamlessly in YouTube Shorts for millions of creators and users, for free. It generates outputs with lower latency at 480p so you can easily create video clips – and for the first time, with sound – from any idea, all from your phone.
This initial launch will allow you to not only generate videos, but also use one video to animate another (or a photo), stylize your video with a single touch, and add objects. You can also create an entire video — complete with voiceover — from a collection of clips, or convert speech to song. All of these features are a bit silly, but, well, that’s often where genius — or at least virality — comes from.
Critics, of course, will label this an AI slop machine, and they’ll be right! The vast majority of content created by these tools will be boring and unwatched. That, however, is already the case with YouTube: the service sees 500 hours of content uploaded every minute, and most of that content isn’t interesting to anyone; the magic of YouTube, however, is the algorithm that finds out what is actually compelling and spreads it to an audience that wants exactly that.
To put it another way, for YouTube AI slop is a strategy credit: given that the service has already mastered organizing overwhelming amounts of content and only surfacing what is good, it, more than anyone else, can handle exponentially more content which, through the sheer force of numbers, will result in an absolute increase of content that is actually compelling.
That’s not the only strategy credit YouTube has; while the cost of producing AI-generated video will likely be lower than the cost of producing human-generated video, at least in the long run, the latter’s costs are not borne by TikTok or Meta (Facebook and Instagram are basically video platforms at this point). Rather, the brilliance of the user-generated content model is that creators post their content for free! This, however, means that AI-generated video is actually more expensive, at least if it’s made on TikTok or Meta’s servers. YouTube, however, pays its creators, which means that for the service AI-generated video actually has the potential to lower costs in the long run, increasing the incentive to leverage DeepMind’s industry-leading models.
In short, while everyone immediately saw how AI could be disruptive to Search, AI is very much a sustaining innovation for YouTube: it increases the amount of compelling content in absolute terms, and it does so with better margins, at least in the long run.
Here’s the million billion trillion dollar question: what is going to matter more in the long run, text or video? Sure, Google would like to dominate everything, but if it had to choose, is it better to dominate video or dominate text? The history of social networking that I documented above suggests that video is, in the long run, much more compelling to many more people.
To put it another way, the things that people in tech and media are interested in has not historically been aligned with what actually makes for the largest service or makes the most money: people like me, or those reading me, care about text and ideas; the services that matter specialize in videos and entertainment, and to the extent that AI matters for the latter YouTube is primed to be the biggest winner, even as the same people who couldn’t understand why Twitter didn’t measure up to Facebook go ga-ga over text generation and coding capabilities.
AI Monetization
The potential impact of AI on YouTube’s fortunes isn’t just about AI-created videos; rather, the most important announcement of last week’s event was the first indicator that AI can massively increase the monetization potential of every video on the streaming service. You might have missed the announcement, because YouTube underplayed it; from their event blog post:
We’re adding updates to brand deals and Shopping to make brand collaborations easier than ever. We’re accelerating these deals through a new initiative and new product features to make sure those partnerships succeed – like the ability to add a link to a brand’s site in Shorts. And YouTube Shopping is expanding to more markets and merchants and getting help from AI to make tagging easier.
It’s just half a sentence — “getting help from AI to make tagging easier” — but the implications of those eight words are profound; here’s how YouTube explained the feature:
We know tagging products can be time-consuming, so to make the experience better for creators, we’re leaning on an AI-powered system to identify the optimal moment a product is mentioned and automatically display the product tag at that time, capturing viewer interest when it’s highest. We’ll also begin testing the ability to automatically identify and tag all eligible products mentioned in your video later this year.
The creator who demonstrated the feature — that right there is a great example of how YouTube is a different world than the one I and other people in the media inhabit — was very enthusiastic about the reduction in hassle and time-savings that would come from using AI to do a menial task like tagging sponsored products; that sounds like AI at its best, freeing up creative people to do what they do best.
There’s no reason, however, why auto-tagging can’t become something much greater; in fact, I already explained the implications of this exact technology in explaining why AI made me bullish on Meta:
This leads to a third medium-term AI-derived benefit that Meta will enjoy: at some point ads will be indistinguishable from content. You can already see the outlines of that given I’ve discussed both generative ads and generative content; they’re the same thing! That image that is personalized to you just might happen to include a sweater or a belt that Meta knows you probably want; simply click-to-buy.
It’s not just generative content, though: AI can figure out what is in other content, including authentic photos and videos. Suddenly every item in that influencer photo can be labeled and linked — provided the supplier bought into the black box, of course — making not just every piece of generative AI a potential ad, but every piece of content period.
The market implications of this are profound. One of the oddities of analyzing digital ad platforms is that some of the most important indicators are counterintuitive; I wrote this spring:
The most optimistic time for Meta’s advertising business is, counter-intuitively, when the price-per-ad is dropping, because that means that impressions are increasing. This means that Meta is creating new long-term revenue opportunities, even as its ads become cost competitive with more of its competitors; it’s also notable that this is the point when previous investor freak-outs have happened.
When I wrote that I was, as I noted in the introduction, feeling more cautious about Meta’s business, given that Reels is built out and the inventory opportunities of Meta AI were not immediately obvious. I realize now, though, that I was distracted by Meta AI: the real impact of AI is to make everything inventory, which is to say that the price-per-ad on Meta will approach $0 for basically forever. Would-be competitors are finding it difficult enough to compete with Meta’s userbase and resources in a probabilisitic world; to do so with basically zero price umbrella seems all-but-impossible.
This analysis was spot-on; I just pointed it at the wrong company. This opportunity to leverage AI to make basically every pixel monetizable absolutely exists for Meta; Meta, however, has to actually develop the models and infrastructure to do it at scale. Google is already there; it was the company universally decried for being slow-moving that announced the first version of this feature last week.
I can’t overstate what a massive opportunity this is: every item in every YouTube video is well on its way to being a monetizable surface. Yes, that may sound dystopian when I put it so baldly, but if you think about it you can see the benefits; I’ve been watching a lot of home improvement videos lately, and it sure would be useful to be able to not just identify but helpfully have a link to buy a lot of the equipment I see, much of which is basically in the background because it’s not the point of the video. It won’t be long until YouTube has that inventory, which it could surface with an affiliate fee link, or make biddable for companies who want to reach primed customers.
More generally, you can actually envision Google pulling this off: the company may have gotten off to a horrible start in the chatbot era, but the company has pulled itself together and is increasingly bringing its model and infrastructure leadership to bear, even as Meta has had to completely overhaul their AI approach after hitting a wall. I’m sure CEO Mark Zuckerberg will figure it out, but Google — surprise! — is the company actually shipping.
A Bull’s Journey
Or, rather, YouTube is. Close readers of Stratechery have been observing — and probably, deservedly, smirking — at this most unexpected evolution:
That quote is from Paradigm Shifts and the Winner’s Curse, an Article that was mostly about my concerns about Apple and Amazon, and reads:
And, by the same token, I’m much more appreciative of Google’s amorphous nature and seeming lack of strategy. That makes them hard to analyze — again, I’ve been honest for years about the challenges I find in understanding Mountain View — but the company successfully navigated one paradigm shift, and is doing much better than I originally expected with this one. Larry Page and Sergey Brin famously weren’t particularly interested in business or in running a company; they just wanted to do cool things with computers in a college-like environment like they had at Stanford. That the company, nearly thirty years later, is still doing cool things with computers in a college-like environment may be maddening to analysts like me who want clarity and efficiency; it also may be the key to not just surviving but winning across multiple paradigms.
Appreciating the benefits of Google being an amorphous blob where no one knows what is going on, least of all leadership, is a big part of my evolution; this Article is the second part: that blob ultimately needs a way to manifest the technology it manages to come up with, and if you were to distill my worries about Google in the age of AI it would be to wonder how the company could become an answer machine — which Page and Brin always wanted — when it risked losing the massive economic benefits that came from empowering users to choose the winners of auctions Google conducted for advertisers.
That, however, is ultimately the text-based world, and there’s a case to be made that, in the long run, it simply won’t matter as much as the world of video. Again, the company is doing better with Search than I expected, and I’ve always been bullish about the impact of AI on the company’s cloud business; the piece I’ve missed, however, is that Google already has the tip of the spear for its AI excellence to actually go supernova: YouTube, the hidden giant in plain sight, a business that is simultaneously unfathomably large, and also just getting started.
