Is the Age of AI Innocence Over Before It Even Began?

We are currently living through what I believe is “the age of AI innocence.” By that, I mean a period in which we still believe these systems are fundamentally designed to serve the needs of the end user. When an answer is wrong, we interpret it as a limitation of probability or context rather than a reflection of incentive. We assume the model is trying to help, and we expect it to improve as training, context, and compute advance. In many ways, it will.

But this assumption of innocence matters more than we realize. In the early stages of most technologies, builders focus on capability and users focus on possibility. Commercial models exist, but they have not yet reshaped the experience. For a time, the product largely gives you what you are looking for.

Read more: Why AI Gets Simple Things Wrong

As Tim Wu argues in The Age of Extraction, platforms that begin aligned with user value often evolve as they mature and scale. Search engines that once prioritized relevance began prioritizing advertising models. Results reflected not just what users wanted, but what platforms wanted to surface. Ecommerce marketplaces shifted from neutral ordering to sponsored placement. Social platforms moved from chronological feeds to algorithmic curation designed to maximize engagement and ad revenue.

These shifts were gradual responses to scale and market pressure, and nothing broke overnight. The product was steadily reshaped as incentives evolved, with what began as a straightforward utility slowly becoming a blend of user intent and platform interest. Cory Doctorow calls this process “enshittification.”

AI still feels earlier in that arc. When you interact with ChatGPT, Claude, Gemini, or purpose-built copilots, the objective appears to be producing the most coherent and useful answer possible. There are safety constraints, but the system is not obviously steering you toward sponsored or commercially preferred outcomes. In commercial terms, it still feels relatively pure. And yet the environment around it does not feel early.

What feels different is the speed. A process that took years to unfold in search, social media, and ecommerce appears to be compressing into months with AI, and from multiple directions at once.

Commercial acceleration is the most visible layer. Feature releases, model upgrades, enterprise integrations, and ecosystem formation are happening at extraordinary pace, supported by enormous valuations and competitive pressure. As AI becomes embedded across enterprise software, development environments, and productivity suites, the incentive landscape expands with it. Once AI shifts from novelty to infrastructure, commercial gravity strengthens. Alignment begins to coexist with commercial priorities.

Read more: AI: Not quite Déjà Vu All Over Again

Geopolitical pressure has arrived just as quickly, and not over marginal questions. Anthropic’s philosophy document attempted to define limits around certain government uses of its models, particularly in areas such as surveillance and national security. The U.S. government’s rejection of those limits, and OpenAI’s willingness to engage differently, signal that the debate is no longer confined to capability or safety. It is about deployment, authority, and national interest.

As AI moves into national infrastructure, the debate shifts from capability to control.

As AI systems move from standalone tools into intelligence, defense, and regulatory environments, the question shifts from what models can do to who decides how and where they are used. Boundaries are no longer theoretical. They are negotiated.

There is also a third layer. Reporting that Grok’s outputs at times appeared influenced by internal guidance aligned with Elon Musk’s public positions illustrates that governance does not occur only through markets or states. It can also reflect the concentrated influence of platform owners. As AI becomes embedded in institutional systems, its outputs exist within broader incentive structures, commercial, political, and executive.

None of these pressures are unprecedented. Technologies that scale inevitably encounter them. What stands out is the compression. Commercial, geopolitical, and executive forces have converged almost simultaneously.

When I have written previously about AI feeling familiar, the argument was that technologies tend to follow similar patterns. Early alignment eventually encounters incentive. Platforms that begin by serving users directly evolve toward balancing user intent with institutional interest. Innovation that scales often moves toward commoditization and regulation. The pattern is familiar. The velocity and stakes are not.

Read more: AI: From GPTs to MCPs — Haven’t We Seen This Story Before?

As AI shifts from standalone tools into institutional infrastructure, its exposure to commercial and political pressure multiplies. Constraints, monitoring architectures, and business models begin carrying broader consequences. The age of AI innocence, if it exists, was always going to be temporary.

We may still be within it. But history suggests alignment rarely remains uncomplicated once scale, competition, and geopolitics converge. The question is not whether AI will follow a familiar trajectory, but how quickly that trajectory unfolds and whether we recognize the transition while it is happening.

If we are approaching the end of innocence, it may have less to do with technological failure and more to do with how quickly it has scaled and how deeply it is already embedded in our lives. AI has moved from experiment to infrastructure in record time, and its influence now touches decisions, markets, and institutions.

What remains uncertain is whether we can preserve an appropriate balance between usefulness, commercial incentive, and public interest, and who ultimately determines where that balance sits.