Brian Stauffer/theispot.com
By the ordinary measures of any new technology, the current wave of generative AI has moved fast. By some estimates, about 2.4 billion people worldwide use generative AI platforms each month, and coding agents have changed how software is written.
Efforts to commercialize the technology have scaled just as fast. AI coding platform Cursor reportedly passed a $2 billion revenue run rate by early 2026, and, as of April 2026, Perplexity was reported to have more than 100 million monthly users across its products by challenging one of the internet’s most entrenched markets: search. And this growth is not confined to AI-native companies. Salesforce’s Agentforce has reached $1.2 billion in annual recurring revenue, Harvey has spread across large law firms, and Shopify has made AI use a baseline expectation across its operations. By many conventional markers, these developments increasingly resemble the early stages of a platform ecosystem.
Yet AI’s larger promise is not to become another successful technology platform. It is to become a true general-purpose technology — like electricity or the internal combustion engine — that reshapes organizations, industries, and, ultimately, the broader economy. Judged against that standard, progress remains shallow.1 The process of complementary innovation, organizational integration, and economywide transformation expected of a general-purpose technology remains in its infancy.
The obvious culprits — immature models, ordinary adoption friction — are real, but they’re not the constraint. Like earlier general-purpose technologies, AI will not become economically transformative simply because it is broadly applicable. It will realize that potential when a surrounding technological, industrial, and institutional architecture enables decentralized organizations to confidently build upon it — in other words, when the technology becomes platformed.
Here, I will explain what platforming entails (the technological, industrial, and institutional architectures a technology needs), why AI remains only partly platformed, and how organizations can innovate and invest effectively while that process is still unfolding.
Platforming a General-Purpose Technology
Scholars have long argued that general-purpose technologies are able to transform economies because they can be applied across many industries while stimulating successive waves of complementary innovation — as was the case with electricity, the steam engine, and the internet.2 By the same token, the potential of such technologies is unusually hard to realize. Broad transformation requires large numbers of independent organizations to make interdependent investments; redesign products, processes, and business models; develop new capabilities; and coordinate despite deep uncertainty about how the technology and its ecosystem will evolve. Therefore, the central challenge is creating the technological, industrial, and institutional conditions under which decentralized organizations can confidently build upon the technology — a process of platforming. Someone has to build that foundation: It is what makes decentralized downstream integration, complementary innovation, and co-invention possible at all.
Electrification illustrates this. Electricity was technologically proven and commercially viable by 1882, yet widespread electrification did not follow for nearly four decades. Technological architecture stabilized when the Niagara Falls hydroelectric power project (1895-1896) confirmed polyphase alternating current at commercial scale. Industrial architecture matured as a division of labor settled among utilities, equipment makers, and financiers, under the regulated utility model that took hold between 1898 and 1907. Institutional architecture followed, with the first comprehensive state public-utility commissions forming in 1907.
As these technological, industrial, and institutional architectures progressively aligned, organizations gained sufficient confidence to invest and experiment, and electrification accelerated. Platforming emerged through the combined efforts of inventors, manufacturers, utilities, financiers, standards bodies, and regulators. Other general-purpose technologies — notably, personal computing — illustrate alternative pathways: Platform leaders, such as Microsoft and Intel, more directly orchestrated these architectures to support ecosystem growth.3
A technology becomes platformed when a surrounding technological, industrial, and institutional architecture creates conditions stable enough for decentralized organizations to confidently build upon it. Platforming reduces uncertainty by stabilizing expectations about how the technology can be used and how it will evolve. It establishes clear lanes for complementary innovation — where to innovate, where to rely on others, and what can be treated as stable — and the governance, rules, and incentives that enable organizations to capture value from their investments while coordinating with others. Decentralized investment and experimentation can then scale from isolated successes into broad transformation through the alignment of the three architectures.
The Platforming of AI: Where Are We Now?
The platforming of AI remains incomplete, but recognizable technological, industrial, and institutional architectures are emerging. Understanding what has stabilized — and what has not — clarifies the opportunities and the frustrations of building on AI before it has been fully platformed. Let’s take a look at the current state of AI.
AI’s technological architecture is still emerging. Today’s dominant AI architecture rests on a relatively specific trajectory, especially among leading frontier developers: pretrained, predominantly language-based foundation models; specialized hardware; cloud-based training and inference; and API-mediated delivery.4 AI is taking shape as a layered stack — chips, cloud infrastructure, foundation models, and the applications built on them. (See “Key Elements of the AI Stack.”) The stack’s lower three layers are converging on a centralized foundation in which model development and most computation reside with a few cloud-hosted frontier models, with most organizations consuming intelligence remotely through APIs rather than owning it. This departs from the digital services economics that were once taken for granted: Rather than distributing software that runs locally at little additional cost, AI delivers intelligence through continual, cloud-hosted inference, performing heavy computation each time intelligence is used.5 Although the prevailing architecture continues to evolve, the likely alternatives — open-weight ecosystems and parallel stacks developed by Chinese companies — are variations on it rather than fundamentally different trajectories. It’s likely that to the extent it continues, much of the uncertainty around the lower layers will subside.
The application and deployment layer, where most organizations hope to build complementary products and services, remains fluid. As emerging orchestration, agent, and middleware layers compete to define how AI should be integrated into products, workflows, and enterprise systems, some companies build around chatbot interfaces and others directly on foundation model APIs.
