For the Hyperscalers, There's No Such Thing as "Spending Too Much on AI"

Today on the AI Daily Brief, why the AI hyperscalers are spending billions and even trillions of dollars building out AI, and why their bet might make sense before that in the headlines, and frankly, quite related, Are AI Models getting totally Commoditized? The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. To join the conversation, follow the Discord link in our show. Not. Welcome back to the AI Daily Brief Headlines edition. All the daily AI news you need in around five minutes. We kick off today with a really interesting conversation that I'm seeing emerging more and more on AI Twitter, which is about model commoditization. Part of the interesting shift that this discussion represents is in the wake of ChatGPT being launched and a million companies getting funding, there has been a pejorative sense in many ways of startups that are simply quote unquote chatgpt rappers. The idea here being that if you're not building your own proprietary model, you have no moat. The interesting question, however, is how much proprietary models actually do create a moat. Take for example this tweet from Sully Omar, an AI entrepreneur. He writes, at the start of 2024 my startup was using 0% Google, 5% Anthropic, 95% OpenAI. Now it's 35% Google and growing 35% Anthropic and 30% OpenAI. We just switched to the cheapest, best model. Maybe no one has a moat after all. Certainly this is my experience as an individual user. I am constantly jumping between whatever model I think is the most performant at any given time, with absolutely nothing resembling any sort of brand loyalty product. Experience does matter. For example, I think that the latest version of GPT4O has in general been more performant to me than even Claude 3.5 sonnet, but the interface of Claude, particularly with artifacts, does make me in many cases try to use that instead of ChatGPT. In any case, this question of whether model builders can actually build a moat around what they're creating, or whether there's just going to be constant shifting sands among people who are willing to switch models is a really interesting question. Of course, enterprise lock in and things like that could be X factors, but it's something that I'm watching closely. In the meantime, new models keep coming out and it's clear that the competition isn't just at the state of the art in the biggest models, but is also about more performance smaller models. Microsoft has released three new Phi 3.5 models. There is Phi 3.5 Mini instruct with 3.82 billion parameters Phi 3.5 Moe instruct which is 41.9 billion parameters and 5.3.5 Vision instruct which is 4.15 billion parameters. Now these are small models that are putting up some really good numbers on benchmark tests, Developer Jan Peleg writes. How the hell is phi 3.5 even possible? Phi 3.5 mini somehow beats llama 3.1 ab phi 3.5 moe somehow beats gemini flash phi 3.5 vision somehow beats gpt4o how lol. Now people haven't had that much of a chance to get their hands on these models yet and many cautioned assuming too much from self published benchmarks. Still, like I said, I think the more interesting thing here, even outside where this leaves these Microsoft models in the rankings, is what they say about the state of competition. Two dimensions of this that are interesting for fi specifically. One is this is yet another sign that Microsoft is doing a heck of a lot of hedging when it comes to its approach to AI, and is very clearly not just resting on its OpenAI relationship. And two, once again, it really does suggest how much of the competition is happening in these smaller models, not just to create the most powerful large model. Nvidia and Mistral have also released a new model called Mistral Nemo Minitron 8B. This comes a month after the two companies teamed up to release Mistral Nemo 12B. This new model was created using something called model pruning and distillation. This they describe this as the process of making a model smaller and leaner, either by dropping layers or dropping neurons and attention heads and embedding channels. Model distillation is a technique used to transfer knowledge from a large, complex model, often called the teacher model, to a smaller, simpler student model. The goal is to create a more efficient model that retains much of the predictive power of the original larger model, while being faster and less resource intensive to run. Now again, part of why these things are interesting and why it's relevant that there is this competition around smaller, more performant models is that it suggests that we're moving strongly into a phase of of real, practical utility and commercialization of generative AI. Companies are racing to build models that can operate on devices and at a cost that works for average consumer use cases. In other words, from a distribution of efforts and time standpoint, a lot more emphasis is going into things that could actually show up in consumer products. And of course, the competition for adoption remains fierce. The Information Today posted an article called Meta's search for AI clout takes it to new terrain the story is basically all about how Meta is having to develop a new skill, which is to get big businesses to buy into their software, the article reads. Zuckerberg wants to turn Meta's LLM Llama 3 into the industry standard for AI. Initially, he has relied mostly on other tech companies to handle selling the software to customers, with mixed results so far. Specifically, they point to Amazon Web Services, which through their bedrock platform offers a variety of different LLMs to their enterprise customers. Right now, however, AWS doesn't appear to be a huge channel for them. According to Insiders, Anthropic's Claude is the most popular model on the platform, which could also represent preferential treatment from aws, who has a huge investment in Anthropic when it comes to the Azure marketplace. The information sources say that salespeople at Microsoft typically only pitch Llama to customers that have existing data expertise, rather than to more general enterprise customers. The rest of the article is all about the different ways that Meta is trying to resolve this situation. And again, for our purposes, it's not so much that there's a big interesting piece of news here, but more that this reflects where a lot of generative AI is going to be in the next year or so, which is much more focused on actual business competition. Speaking of models and commoditization, there are now so many great image generation models and perhaps not surprisingly, part of the battle is moving to user interface. On that front, Midjourney has announced that their web experience is now open to everyone, meaning you no longer have to go through discord to use it. And in addition to that, they're even turning on temporary free trials, which is something they haven't had for quite some time. Ideogram Meanwhile, released ideogram 2.0, which I have not yet had a chance to try, but which has a ton of people so far really impressed. The AI for Success account on Twitter writes ideogram2 is by far the best model for handling text and AI images and can easily handle 15 to 20 words. You can now make memes, posters and even create YouTube thumbnails and more in seconds. Anyways, lots of goodies out there for the image generation folks to try. And finally today, a cool story from eleven Labs. The company has announced an impact program with a vision to empower quote 1 million new voices to communicate, learn and experience life without limits. This is basically a nonprofit partner program that provides free licenses for anything from enhancing accessibility, advancing education for those in need, or improving shared cultural experiences. By way of example, they shared their first initiative, a partnership with Bridging Voice and the Scott Morgan foundation, focused on helping people who are losing their voice to ALS or MND create copies of their voices that match their natural speech so they can retain that voice even if the disease progresses to a place that makes communication in a traditional way impossible. Pretty cool little initiative. Glad to see companies like eleven Labs doing this. For now though, that is going to do it for today's AI Daily Brief headlines Next up, the main episode. Today's episode is brought to you by Plum. 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If you trust all the company's hackers and NSA board members that will ever have access to your AI conversations, then rejoice, for you are well served. For the rest of us, Venice is an alternative. Venice is a powerful AI app for text, image and code generation that respects you as a sovereign individual and believes privacy and free speech are not only human rights but necessary for civilizational advancement. Private, permissionless and uncensored, you can try it for free without an account. AI Daily Brief listeners receive a 20% discount on Venice Pro. Visit Venice AI NLW and enter the discount code nlwdailybrief. That's NLW Daily Brief. All one word. Welcome back to the AI Daily Brief. It's slightly quieter today, assuming you're not just in a world of finally using Midjourney's web features. And I just came across this great essay in VC Sarah Tavel's newsletter that deals with a question that we've been discussing all summer. That question is, of course, the AI Bubble question. Specifically when it comes to valuations and money, right? As we've discussed lots of times, there is a big difference between discussing whether Wall street is pricing AI correctly and whether that is a bubble versus whether AI itself, as a technology and as a disruptive technology, is overhyped. You may remember the essay that I discussed from David Kahn at Sequoia called AI $600 billion question, which by the way, wasn't nearly as negative as people tried to make it out. And then of course there was Goldman Sachs report Genai Too Much spend, too little Benefit. I spent a lot of time dissecting those two pieces and in particular took umbrage with the interview with Jim Cavello, the head of global equity research at Goldman Sachs, who, who made the very bold claim that, quote, the tech world is too complacent in its assumption that AI costs will decline substantially over time and that the starting point for costs is also so high that even if costs decline, they would have to do so dramatically to make automating tasks with AI affordable. Today we are going to read this essay and it will be me actually reading no AI, and then we will come back and talk about what it adds to this overall discussion. Sarah writes, I'm sure you read David Kahn's provocative piece AI $600 billion question, in which she argues that given Nvidia's projected Q4 2024 revenue run rate of 150 billion, the amount of AI revenue required to pay back the enormous investment being made to train and run large language models is now $600 billion and we are at least $100 billion in the hole on that payback. The numbers are certainly staggering and are just going to get bigger until we reach an efficient frontier of the marginal value of adding more compute or we hit some other roadblock that causes people to lose faith in the current architecture. This is a contest now of not blinking first. If you're a big stack player like Microsoft, Google or any of the other foundation model pure plays, you have no choice but to keep raising your bet. The prize and power of winning is too great. If you blink, you are left empty handed watching someone else count your chips. It's likely hundreds of billions will be destroyed and trillions earned too early to know who the winner or losers are. But for all of us in the startup ecosystem, among many things, it's going to create new waves of AI opportunities taking a step back as LLMs progress, they are able to handle more complicated tasks. Many of the foundation model companies talk about the amount of time it would take a human to do the work. As a measure of the power of the LLM, if today LLMs can handle tasks that would have taken a human five minutes to complete. As LLMs progress, they'll be able to handle increasingly complicated tasks that would have taken a human more time. In the next decade, the belief is that they'll be able to handle tasks that would take years for a human to do. Therefore, as the LLMs become more and more sophisticated, the economic value that they will be able to unlock becomes greater and greater. For example, annually it is estimated that we spend $1 trillion on software engineers globally. When people talk about GitHub Copilot, you hear people throw around numbers like 10 to 20% productivity improvements. Of course GitHub claims higher. That translates to 100 to $200 billion of value annually were it to be fully deployed, of which GitHub would capture some percentage. Indeed, Copilot is likely already a multi billion dollar revenue line for Microsoft. As LLMs progress and are able to go beyond code completion like Copilot, there is almost no limit in value creation as it would dramatically expand the market. A potential multi trillion dollar opportunity if someone emerges as a dominant player. And that's just coding. We've all experienced the productivity improving benefits of LLMs or been on the receiving end of an automated customer support response. The potential value creation and capture with AI is beyond our existing mental models. The challenge is the amount of capital required to train each successively more sophisticated LLM increases by an order of magnitude, and once a model is leapfrogged by another, the pricing power of the older model quickly falls to zero. There are now more 3.5 equivalents for a developer to choose from. Not surprisingly, when GPT 3.5 launched in November 2022, it was head and shoulders ahead of any competitive model and cost 2 cents for a thousand tokens. It's now 5/100 of a cent 2.5% of its original pricing in just 1 1/2 years. I can't remember another technology that has commoditized as quickly as LLMs. It's a dynamic that makes it almost impossible to rationalize any ROI at this stage in the game, because any investment in an LLM is almost instantly depreciated by the next version, but you can't really skip a step. You need to go through countless worthless versions to get to the ultimate the idealized AGI. So you have a bit of a perfect storm. One the economic model you are able to unlock as models become more sophisticated should increase Significantly, with each upgrade of the model, the economic value of AGI is constrained only by our imaginations. 2. Pricing leverage comes from being a step function ahead of the competition, at least along some dimension. If you fall behind, the value of your model to external customers gets rapidly commoditized. Of course there is still value for your internal use cases. 3. Microsoft, Google and Meta have core businesses that produce fire hydrants of cash. Anthropic has found love with Google and Amazon and OpenAI should continue to be able to raise money from sovereigns that have their own more physical fire hydrants of cash. The net result is that in the short term, until an efficient frontier is reached on the marginal value of continuing to invest in infrastructure with the existing transformer architecture, or we run out of electricity, or a group pulls ahead with an untouchable lead thanks to some smart algorithmic work, investment in this space by these giants should continue to increase dramatically and costs necessarily precede revenue. The prize is theoretically so large and if a clear winner emerges, their market opportunity so uncapped, you have to keep increasing your bet. We are all massive beneficiaries of this battle playing out the extreme pace of investment in infrastructure, training, et cetera, combined with the urgency that only comes from intense competition, is giving us all the gift of an insane pace of innovation. With models that are able to handle increasingly complicated tasks at bargain basement prices, applications that might not be possible today, let alone economic, such as most voice and video applications, will be profitable before we know it. Giddy up. All right, so back to NLW here. First, thanks to Sarah for a great and provocative piece. Two things that I want to hone in on. Sarah breaks this apart into what it means for them and what it means for us, which is something that people don't do enough when it comes to what this means for them, specifically the hyperscalers. Sarah argues basically the same thing that I argued in my previous refutation of those pieces when she writes the prize is theoretically so large and if a clear winner emerges, their market opportunity so uncapped, you have to keep increasing your bet. This is the logic that all of this investment is based on. Part of the reason that I've said Wall street is so uncomfortable with trying to price this is that the approach of these companies is forcing Wall street to think like a venture capitalist instead of like a Wall street investor. How to work backwards from and handicap the odds of reaching some new, totally different economic paradigm is just not an easy thing to do. And so I anticipate that there will continue to be debates effectively for as long as it takes to get to the other side of AGI around whether this is money well spent or not. My strong suspicion is in fact that in many cases these debates will tell us less about how investors are feeling about AI and a lot more about how they're feeling about everything else. In other words, I think that part of the reason that we're seeing some fatigue in the AI narrative right now, in fact a lot of the reason has nothing to do with AI itself, and everything to do with the fact that Wall street has a new narrative champion in forthcoming Federal Reserve rate cuts. Remember, the entire period of the post chatgpt AI boom on Wall street has happened during the Fed's hiking cycle and then higher for longer cycle. Wall street has often clung to the AI narrative as a counterbalance to the negative implications of those higher rates. Now that rates are going to start coming down again, Wall street feels more comfortable jettisoning some of those narratives. As is so often the case, the vibe shift potentially tells us a lot more about the vibe feeler than about the vibe creator. The second piece of this, though, that I want to hone in on is the point that she makes in the concluding paragraph, which is so salient that we are the beneficiaries of all of this, playing out that the extraordinary amount of competition which is driving prices down so quickly, increasing capacity so quickly, is creating an unbelievably fertile landscape for building. Solopreneurs who are hacking together applications that never would have been possible before without venture capital are feeling it. The venture sector of startups who are getting to slosh around and experiment with totally new paradigms of human computer interactions, are feeling it. And enterprises, while stumbling over themselves with lots of false starts and proofs of concept and concerns around ROI are also, for the first time in a long time, really starting to sniff out how a new category of technology can actually transform how they operate, operate, and what they can achieve. In other words, rather than lamenting the gobs and gobs of cash that the foundation models are throwing at this space, there's something to be said for just enjoying and frankly creating the positive externalities of all that. Anyways, once again, big thank you to Sarah for her newsletter. If you want to find more, you can go to SarahTavel. That's t a v e l dot com and that's going to do it for today's AI Daily Brief. Thanks for listening or watching. As always, and until next time. Peace. Sam.