I read the article and while it doesn’t say this nor imply it, this is my takeaway, though correct me if I’m wrong:<p>Model innovation is effectively converging and slowing down considerably. The big companies in this space doing the research are not making leap over leap with each release, and the downstream open source projects are coming closer to the same quality or in fact can produce the same quality (e.g DeepSeek or LLAMA) hence why it’s becoming a commodity.<p>Around the edges model innovation - particularly speed ups in returning accurate results - will help companies differentiate but fundamentally, all this tech is shovels in search of miners, IE you aren’t really going to make money hand over fist by simply being an LLM model provider.<p>In another words, this latest innovation has hit commodity level within a few short years of going mainstream and the winners are going to be the companies that make products on top of this tech, and as the tech continues to become a commodity, the value proposition for pure research companies drops considerably relative to application builders.<p>To me this leaves a central question: when does it hit a relative equilibrium where the technology and the applications on top of it have largely hit their maximal ability to add utility to applicable situations? That’s the next question, and I think the far more important one<p>One other thing, at the end of the article they wrote:<p>>Ultimately, businesses won’t rearrange themselves around AI — the AI systems will have to meet businesses where they are.<p>This is demonstrably untrue. CEOs are chomping at the bit to reorganize their business around AI, as in, AI doing things humans used to do and getting the same effective results or better, thereby they can reduce staff across the board while supposedly maintaining the same output or better.<p>Look at the leaked Shopify memo for an example or the trend of <i>“I can vibe code with an LLM making software engineers obsolete”</i> that has taken off as of late, if LinkedIn is to be believed
This take doesn't really highlight the fact that the most competitive foundational model companies <i>are</i> innovative application builders. Anthropic and OpenAI are vying for consumers to use their models by building these sort of super applications (ChatGPT, Claude) that can run code, plot graphs, spin up text editors, create geographic maps, etc. These are well staffed and strategically important areas of their businesses. There's competition to attract consumers to these apps and they will grow more capable and commoditize more compliments along the way. Who needs Jasper when you can edit copy in ChatGPT, or an AI python notebook app, or, now, Cursor?
One thing this article gets wrong is how OpenAI isn’t an application layer company, they built the original ChatGPT “app” with model innovation to power it. They’re good at UX and actually have the strongest shot at owning the most common apps (like codegen).
There's a lot of opportunity to apply leading edge AI models to specific business applications, but success here is determined more by experience with those business domains than with AI generally.<p>An AI startup could still be a useful "resume" to get acqui/hired by one of the big players.
The LLM space was never going to be kind to those without deep pockets. And right now there's no point getting in it because it's hit a wall. So yeah, startups should steer clear of trying to make frontier LLM models.<p>On the other hand, there's a ton of hype and money looking for the next AI related thing. If someone creates the next transformer, or a different AI paradigm that pushes things forward, they'll get billions.
Every startup should generate a shitty Ai wrapper product, write one or two lines of code, generate hype and have 2025's version of Softbank give you a billion $'s.<p>Frankly it's bordering on irresponsible to not be targeting acquisition in this climate.
This focuses on case where the acquirer seeks to capture the value of the startup's business. But this is not always the case, sometimes the startup is dubious, but a cash-rich enterprise can purchase startups simply to eliminate potential avenues of competition. They may not be interested in adding a better product to their portfolio, only in quashing any nascent attempts at building the better product so they can keep selling their own mediocre one.<p>Also, "model innovation" strikes me as missing the point these days. The models are really good already. The majority of applications is capturing only a tiny bit of their value. Improving the models is not that important because model capability is not the bottleneck anymore, what matters is how the model is used. We just don't have enough tools to use them fully, and what we have is not even close to penetrating the market, while all the dominant tools are garbage. Of course application innovation is the place to be!
Work just to be a part of it. This is the most consequential time in history.<p>It's the best time ever to build. Don't work on anything that could have been done two years ago.<p>Learn the current tools - so that you can adapt to the new tools that much faster as they come out.
The fact that a VSCode-based GPT-wrapper is being offered $3B tells you how desperate the LLM companies are.<p>Anthropic and xAI will also make similar acquisitions to increase their token usage.