AI has gone through repeated cycles of euphoria and disappointment since its inception. AI itself has never been crisply defined, roughly encompassing tasks that could previously only be performed by humans, done by machines. However, tasks once considered AI tend to lose the label once they become reliable and commoditized, e.g., OCR or classic search-based chess engines.

At a high level, AI includes two broad genres of approaches: one largely deterministic; the other stochastic. The first encompasses for example, various expert systems and search-based engines to solve games. The latter, at their core, revolve around building statistical models from data. One particularly notable aspect was that stochastic models were both hugely profitable (e.g., modern ad serving) but also a far cry from AI’s original aspirations, after all, no human can meaningfully reason about such probabilities at scale. All of this changed in the recent AI wave, as ML-based stochastic models found firm ground across many real-world use cases, driven by advances in large neural networks. We are now in another phase of AI euphoria.

When we think about machine learning, it is often framed as approximating a complex function from input to output. A typical ML training setup requires the availability of well-labeled data, since machines need examples to learn. This also unintentionally limits such systems, as it is often extremely expensive to curate large amounts of well-labeled data.

The recent AI renaissance is a perfect storm driven by the explosion in data volume, model size, and the availability of vast amounts of computational power. Three important changes induced this explosion: the use of proxy training objectives, the rise of foundation models, and the widespread availability of GPUs. Objectives such as next-token prediction or diffusion-based training unlocked access to massive amounts of previously unlabeled data, including the text and images freely available on the web. These models are still statistical at their core, but something qualitatively changed in how they generalize and interface with the world. In a limited but important sense, these foundation models are models of the world. A key enabler of this shift was the rapid advance and commoditization of GPUs, originally driven by graphics and later accelerated by cryptocurrency mining, making large-scale parallel computation economically feasible.

What comes next? A trillion dollar question. We are now at a stage of world-model building. So far, these world models appear to exhibit a wide range of emergent capabilities, sometimes perceived as almost magical. This has given rise to zero-shot learning, prompt engineering, and a seemingly endless range of applications built on top of these models. However, it is important to remember that these systems retain a fundamentally stochastic core.

I take no position on how close we are to AGI, as that would require answering what human intelligence actually is, a question far beyond the scope of this discussion. The concrete theme of the next steps for such world models is grounding them within specific domains. This can take two forms: either creating a deterministic shell around a powerful stochastic machine, or applying it directly in domains that are themselves stochastic in nature.

The first category is predominantly coding, at least initially, as it is comparatively easy to ground. We are at the beginning of a seismic change in coding as a profession. To a large degree, this is very similar to past abstraction changes in software development, from machine code to assembly, to system programming languages like C, to languages with automatic garbage collection, and to more descriptive languages in data processing (SQL) and web technologies (HTML, CSS and JavaScript). Those building blocks at lower layer of technical stack won’t disappear, but they will become far less economically sustainable in application domain. Many business applications will be built primarily using AI-assisted or AI-driven approaches; while these systems may be less efficient in execution, they will be significantly more economical to develop.

The second category covers much of today’s white-collar work. Within this category, there are two subcategories: domains that are inherently stochastic, often described as creative, and that tolerate loose error bounds. Beyond ads and recommendations, think recruiting, marketing, first-level customer service, design, and similar functions. In these domains, even exposing the stochastic core directly is likely to work well, making them among the first to be disrupted. The second type involves domains with more deterministic rules and consequences. Disrupting these domains will take longer and will likely require substantial grounding, in the form of a deterministic outer shell. As with most technological shifts, domains with higher profit margins will attract attention first.

What are the implications for today’s SaaS companies? They can apply the above framing and start investing in AI to mitigate the risk of disruption. Incumbents still retain the advantage of deep domain understanding, but if a domain is profitable enough, a competitor will eventually emerge that approaches the problem very differently in an AI-first fashion. The relevant question is not whether to adopt AI, but whether any durable moat remains once development costs collapse and profitable domains inevitably attract AI-first competitors.