What a great idea I had for the first Plaintext of 2025. After following the frantic competition between OpenAI, Google, Meta, and Anthropic to churn out brainier and deeper “frontier” foundation models, I settled on a thesis about what’s ahead: In the new year, those mighty trailblazers will consume billions of dollars, countless gigawatts, and all the silicon Nvidia can muster in their pursuit of AGI. We’ll be bombarded by press releases boasting advanced reasoning, more tokens, and maybe even assurances that their models won’t make up crazy facts.
But people are tired of hearing about how AI is transformational and seeing few transformations to their day-to-day existence. Getting an AI summary of Google search results or having Facebook ask if you want to pose a follow-up question on a post doesn’t make you a traveler to the neo-human future. That could begin to change. In ’25 the most interesting AI steeplechase will involve innovators who set about making the models useful to a wider audience.
You didn’t read that take from me in the first week of January because I felt compelled to address topics related to the newsworthy nexus between tech and Trump. In the meantime, DeepSeek happened. This is the Chinese AI model that matched some of the capabilities of the flagship creations of OpenAI and others, allegedly at a fraction of the training costs. The lords of giant AI now insist that building ever bigger models is more critical than ever to maintain US primacy, but DeepSeek lowered the barriers for entry into the AI market. Some pundits even opined that LLMs would become commodities, albeit high-value ones. If that’s the case, my thesis—that the most interesting race this year would be between applications that brought AI to a wider audience— has already been vindicated. Before I published it!
I do think the situation is fairly nuanced. The billions of dollars that AI leaders plan to spend on bigger models may indeed trigger earth-shattering leaps in the technology, though the economics of centibillion-dollar AI investments remain fuzzy. But I’m more confident than ever that in 2025 we’ll be seeing a scramble to produce apps that make even skeptics admit that generative AI is at least as big a deal as smartphones.
Steve Jang, a VC who has a lot of skin in the AI game (Perplexity AI, Particle, and—oops—Humane) agrees. DeepSeek is accelerating, he says, “a commoditization of the extremely high-value LLM model lab world.” He provides some recent historical context: Soon after the first consumer transformer-based models like ChatGPT appeared in 2022, those trying to provide use cases for actual people concocted fast-and-dirty apps on top of the LLMs. In 2023, he says, “AI wrappers” dominated. But last year saw the rise of a countermovement, one where startups attempted to go much deeper to create amazing products. “There was this argument, ‘Are you a thin wrapper around AI, or are you actually a substantial product in your own right?’” Jang explains. “‘Are you doing something truly unique while using at your core these AI models?’”
That question has been answered: Wrappers are no longer the industry delight. Just as the iPhone went into overdrive when the ecosystem shifted from clunky web apps to powerful native apps, the AI market winners will be those that dig deep to exploit every aspect of this new technology. The products we’ve seen so far have barely scratched the surface of what’s possible. There’s still no Uber of AI. But just as it took some time to mine the possibilities of the iPhone, the opportunity is there for those poised to seize it. “If you just hit pause on everything, we probably have five to 10 years worth of capabilities we could turn into new products,” says Josh Woodward, the head of Google Labs—a unit that cooks up AI products. In late 2023, his team produced Notebook LM, a writer’s support tool that’s way more than a wrapper and has won a rabid following of late. (Though too much of the attention has focused on a feature that transforms all your notes into a gee-whizzy conversation by two robot podcast hosts, a stunt that unintentionally underlines the vapidity of most podcasts.)
