Stochastic Flocks and the Critical Problem of 'Useful' AI | TechPolicy.PressPerspective<br>Stochastic Flocks and the Critical Problem of 'Useful' AI<br>Eryk Salvaggio / Feb 22, 2026The Parliament of Birds, an 18th-century oil painting by Karl Wilhelm de Hamilton Wikimedia

AI technology is advancing. Anyone thinking critically about large language models and their impact on society now faces a more complex challenge: the agentic turn.<br>In the industry, agentic AI refers to ideal systems that “plan”: generating code that writes more code, executing multi-step actions across apps and models, and adapting autonomously. Agents are sold less as systems that know things but as systems that build things. Rather than just generating text or other media in response to a prompt, agentic systems produce code: custom software designed to take action in the world. Structural innovations, such as producing many more outputs and averaging or verifying the results with other agents, are addressing some of the reliability problems that plagued earlier models. Code shifts LLMs to a domain where failures are presumed legible and correctable.<br>These developments are producing real improvements in the LLM's user experience — but is that truly a vindication of the AI project? The press has no shortage of tone-shift think pieces arguing that these new models are transformative and demand new conceptual frameworks to consider their impact. Crucially, two of AI’s heaviest hitters, OpenAI and Anthropic, are said to be ramping up for initial public offerings in the coming months; we should, of course, expect an escalation of hype as the rivals attempt to inflate the market's valuation. But we should also expect true advances in the models they’re selling.<br>These two things can be true at once. The products of these firms feel more responsive and capable, efficiently addressing complex tasks. They can write code to solve problems, and keep rewriting it until the code works. This is not a moment to deny what seems clear to many users, but rather a time to emphasize that the underlying concerns of critical work remains, despite any popular consensus about the technology’s ‘usefulness.’<br>The critical position<br>In a foundational 2021 paper, Emily M. Bender, Timnit Gebru, Angelina McMillan-Major and Margaret Mitchell described LLMs as stochastic parrots—systems that reproduce statistically likely patterns from training data. The frame holds. But now these systems are more complex and even more inscrutable, and the temptation to attribute thoughtful intent to text must now be extended to code.<br>Agentic systems stack these parrots into interacting outputs — a stochastic flock. It is similar to high-frequency stock-trading algorithms, but for the production of code and language. (Appropriately, the plural noun for a flock of parrots is a pandemonium.)<br>The user experience and applications of the technology may change, but the foundational incapacity for accountability, and the underlying ideological and material infrastructures of the AI industry, remain. The paper’s central question, “Can Language Models Be Too Big?” is as relevant as it was five years ago with massive investments into data collection and processing. Other researchers who laid the groundwork to identify algorithmic bias, track the environmental costs of training, challenge the labor practices of data production, or rightfully resist the uncritical adoption of AI in academia are not simply saying no for the sake of it. They hold forth the minimum human conditions for its deployment and remain relevant. The flock only compounds the concerns of bias, false attribution of mind, and inefficiency.<br>Distinguishing system critique from model evaluation is not a concession to hype: it means focusing on collective benefits and harms rather than individual uses. We can talk about what models cannot or should not do without denying what they can do.<br>Implications of agentic AI<br>Waves of slopware<br>One product of the agentic turn is slopware: AI-generated software applications produced faster than they can be meaningfully reviewed, often aimed at short-term problems. Code produced this way typically centers the user's needs over all else. Distribute this software to other users, or allow it to interact, and you create a condition similar to an unregulated airspace, with swarms of individual, disconnected decisions creating pandemonium.

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Code may seem to work in narrow circumstances through all sorts of hacks that mask underlying mistakes. Unlike malware, slopware is not intentionally disruptive; it disrupts through negligence: it’s the hard-coded variable that makes a single man's household finance calculator work, but leads to overdraft fees when used by a single mom. Designing software requires a soft...