Power to the people: How LLMs flip the script on technology diffusion
Transformative technologies usually follow a top-down diffusion path: originating in government or military contexts, passing through corporations, and eventually reaching individuals - think electricity, cryptography, computers, flight, the internet, or GPS. This progression feels intuitive, new and powerful technologies are usually scarce, capital-intensive, and their use requires specialized technical expertise in the early stages.
So it strikes me as quite unique and remarkable that LLMs display a dramatic reversal of this pattern - they generate disproportionate benefit for regular people, while their impact is a lot more muted and lagging in corporations and governments. ChatGPT is the fastest growing consumer application in history, with 400 million weekly active users who use it for writing, coding, translation, tutoring, summarization, deep research, brainstorming, etc. This isn't a minor upgrade to what existed before, it is a major multiplier to an individual's power level across a broad range of capabilities. And the barrier to use is incredibly low - the models are cheap (free, even), fast, available to anyone on demand behind a url (or even local machine), and they speak anyone's native language, including tone, slang or emoji. This is insane. As far as I can tell, the average person has never experienced a technological unlock this dramatic, this fast.
Why then are the benefits a lot more muted in the corporate and government realms? I think the first reason is that LLMs offer a very specific profile of capability - that of merely quasi-expert knowledge/performance, but simultaneously across a very wide variety of domains. In other words, they are simultaneously versatile but also shallow and fallible. Meanwhile, an organization's unique superpower is the ability to concentrate diverse expertise into a single entity by employing engineers, researchers, analysts, lawyers, marketers, etc. While LLMs can certainly make these experts more efficient individually (e.g. drafting initial legal clauses, generating boilerplate code, etc.), the improvement to the organization takes the form of becoming a bit better at the things it could already do. In contrast, an individual will usually only be an expert in at most one thing, so the broad quasi-expertise offered by the LLM fundamentally allows them to do things they couldn't do before. People can now vibe code apps. They can approach legal documents. They can grok esoteric research papers. They can do data analytics. They can generate multimodal content for branding and marketing. They can do all of this at an adequate capability without involving an additional expert.
Second, organizations deal with problems of a lot greater complexity and necessary coordination, think: various integrations, legacy systems, corporate brand or style guides, stringent security protocols, privacy considerations, internationalization, regulatory compliance and legal risk. There are a lot more variables, a lot more constraints, a lot more considerations, and a lot lower margin for error. It's not so easy to put all of it into a context window. You can't just vibe code something. You might be one disastrous hallucination away from losing your job. And third, there is the well-documented inertia of a larger organization, featuring culture, historical precedents, political turf wars that escalate in periods of rapid change, communication overhead, re-training challenges of a distributed workforce and good old-fashioned bureaucracy. These are major headwinds when it comes to rapid adoption of a sparkling new, versatile-but-shallow-and-fallible tool. I don't wish to downplay the impacts of LLMs in corporations or governments, but at least for the moment and in aggregate across society, they have been significantly more life altering for individuals than they have been for organizations. Mary, Jim and Joes are experiencing the majority of the benefit, not Google or the government of the United States.
Looking forward, the continued diffusion of LLMs of course depends on continued performance improvement and its capability profile. The "benefit distribution" overall is particularly interesting to chart, and depends heavily on the dynamic range of the performance as a function of capital expenditure. Today, frontier-grade LLM performance is very accessible and cheap. Beyond this point, you cannot spend a marginal dollar to get better performance, reliability or autonomy. Money can't buy better ChatGPT. Bill Gates talks to GPT 4o just like you do. But can this be expected to last? Train-time scaling (increase parameters, data), test-time scaling (increase time) and model ensembles (increase batch) are forces increasing the dynamic range. On the other hand, model distillation (the ability to train disproportionately powerful small models by training to mimic the big model) has been a force decreasing dynamic range. Certainly, the moment money can buy dramatically better ChatGPT, things change. Large organizations get to concentrate their vast resources to buy more intelligence. And within the category of "individual" too, the elite may once again split away from the rest of society. Their child will be tutored by GPT-8-pro-max-high, yours by GPT-6 mini.
But at least at this moment in time, we find ourselves in a unique and unprecedented situation in the history of technology. If you go back through various sci-fi you'll see that very few would have predicted that the AI revolution would feature this progression. It was supposed to be a top secret government megabrain project wielded by the generals, not ChatGPT appearing basically overnight and for free on a device already in everyone's pocket. Remember that William Gibson quote "The future is already here, it's just not evenly distributed"? Surprise - the future is already here, and it is shockingly distributed. Power to the people. Personally, I love it.
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