The Artificiality of Alignment: Why AI Safety Feels Mismatched to Present Risks

TL;DR

• Credulous, breathless coverage of AI existential risk conflates speculative futures with real present-day harms. The Gradient discusses how the discourse can be muddled by sensationalism.
• The current trajectory of so-called alignment research appears under-equipped for the reality that AI might cause widespread, concrete, and acute suffering today. This misalignment is linked to the “financial sidequest” of building sellable products.
• Commercial incentives shape which problems get pursued and how safety work is framed, with OpenAI and Anthropic at the center as they commercialize powerful models and emphasize product-oriented safety work. [1]
• The alignment toolkit—preferring models of human preferences via RLHF and RLAIF (Constitutional AI)—is presented as addressing a technical problem, but the essay questions whether these approaches capture the most urgent risks.
• A reframing is urged: treat alignment as a technical problem tied to real-world harms rather than solely as a defense against distant catastrophe.

Context and background

The essay opens by noting that credulous, headlining coverage of AI existential risk has reached the mainstream, yet much public discourse muddles speculative future danger with concrete present-day harms. It also argues that, technically, there is a confusing dichotomy between large, “intelligence-approximating” models and traditional algorithmic and statistical decision-making systems. This distinction matters because the risks most people experience today are not necessarily the same kind of risk as the doomsday scenarios some safety researchers warn about. The Gradient contextualizes how the current path of AI safety work is oriented around preventing near-term harms that could arise from powerful systems, yet the field is often framed as preventing humanity’s extinction. The essay contends that the present trajectory of alignment research seems under-equipped — or even misaligned — for the reality that AI might cause widespread, concrete, and acute suffering. It argues that the field has become entangled with the problem of producing a product that people will pay for, a dynamic that can inadvertently fuel doomsday narratives by heightening incentives to push capabilities forward quickly. The author acknowledges the actual usefulness and power of models from OpenAI and Anthropic while emphasizing that their commercial and product orientations shape governance, design decisions, and safety claims. [2][3] A central thread is the idea that many existential-risk advocates extend a belief that AI will eventually surpass human reasoning and could reach “superintelligence.” This framing suggests that alignment becomes a rapid, technical singularity problem. The essay notes that a broader EA ecosystem (e.g., 80,000 Hours) has highlighted alignment and technical research as a high-impact path, but it also cautions that the organizational incentives behind these efforts may complicate how one assesses safety priorities. [4] In the NYT interview referenced, Nick Bostrom characterizes alignment as a theoretical, technical problem, while the piece wonders what it means to define “we” and what “we” seek to achieve when that “we” is largely private companies and their investors. [5] OpenAI and Anthropic are singled out as entities that publicly center superintelligence as a goal, and yet they continue to operate as product-driven firms with revenue imperatives. A notable portion of the piece traces the vocabulary of AI safety to communities around LessWrong and the AI Alignment Forum, where concepts like intent alignment have been formalized. Paul Christiano’s 2018 Medium post defined intent alignment as AI trying to do what humans want it to do. From there, the field has developed a line of research focused on shaping the behavior of AI systems to align with human values. The core practical approach for current systems involves building a base, pre-trained model, then constructing a model of human preferences that critiques and improves the base model’s outputs. For the OpenAI and Anthropic pipelines, this preference model is tuned toward principles described as “helpfulness, harmlessness, and honesty” (HHH). [6] In short, the essay argues that the safety community’s emphasis on alignment is heavily influenced by the business realities of the most powerful players, and that the technical path being pursued may not directly address the most pressing, present-day harms. The piece invites readers to consider whether the emphasis on long-horizon catastrophes overshadows the need to reduce real-world suffering caused by current AI deployments. The essay also foregrounds that the work around RLHF (reinforcement learning from human feedback) and its successor, RLAIF or Constitutional AI, is central to how OpenAI’s and Anthropic’s systems are steered. The concept is to start with a capable base model, gather a model of human preferences, and then steer outputs toward a target set of values. In this framing, the preference model is aligned to the overarching values of helpfulness, harmlessness, and honesty. The piece notes that this workflow arises in an active industry context and is being developed with the intention of producing models that are both useful and safer, while acknowledging the broader safety critique that current methods may not capture all dimensions of risk. [6] The Gradient

What’s new

What’s presented as novel here is not a new experimental result but a reframing: the essay argues that the AI safety discourse treats alignment as a near-term, technical problem that should forestall catastrophe, yet the most influential actors are corporate developers whose primary objective is to commercialize capabilities. The piece highlights a tension between the pursuit of safety innovation and the incentives to release, monetize, and scale products rapidly. The argument is that this very tension can distort which alignment problems are pursued and how safety claims are framed, potentially prioritizing market share over catastrophe-avoidance. The author acknowledges genuine technical interest and collaboration within OpenAI and Anthropic, but stresses that the governance, product decisions, and performance metrics of those organizations will be shaped by revenue considerations. The essay also emphasizes that the safety discourse benefits from a clear vocabulary around “intent alignment” and related approaches, but questions whether these tools are sufficient to avert large-scale, present-day harms when deployed widely. [1][2][3]

Why it matters (impact for developers/enterprises)

For developers and enterprises, the piece offers a warning and a set of signals:

• Alignment research intersects with business strategy. When revenue generation becomes a principal objective, alignment claims can become intertwined with product milestones and growth targets, potentially influencing how safety is measured and enforced.
• The use of RLHF and RLAIF relies on a model of human preferences that is not necessarily representative of all users or contexts. The HH H framework is a guiding principle, but it remains a modeling choice that may not capture every risk dimension in deployment at scale.
• The discourse around superintelligence can shape public expectations and policy incentives in ways that affect funding, regulation, and the pace of experimentation. A broader set of stakeholders and viewpoints may be needed to ground safety work in present-day harms while remaining accountable to real-world outcomes.
• The essay invites engineers and product teams to consider safety as an ongoing engineering problem—not only a philosophical or theoretical concern—by focusing on how alignment pipelines translate into actual user experiences, and what trade-offs exist between safety and usefulness in production systems. [6]

Technical details or Implementation (what’s actually done today)

• Intent alignment and human preferences: The piece discusses the concept of intent alignment, defined as the aim that an AI system should do what humans want it to do. This reframing makes the problem appear more tractable as a technical challenge, enabling a pipeline of optimization around human values. [5]
• Preference models and safety feedback loops: The central idea is to begin with a powerful, pre-trained base model that might generate imperfect outputs, and then fit a separate machine learning model—deemed a “preference model”—to predict human preferences. The preference model is used to critique and improve the base model’s outputs, driving the system toward outputs deemed more helpful, harmless, and honest (HHH). [6]
• RLHF and RLAIF / Constitutional AI: These are the principal methods discussed for aligning models with human values. RLHF uses human feedback to shape the model’s behavior, while RLAIF (also known as Constitutional AI) uses AI-augmented feedback to refine outputs toward the HH H criteria. The practical upshot is an iterative loop in which the base model is continuously guided by preference signals to produce outputs that align more closely with stated human values. [6]
• Context and platforms: The essay situates these techniques within a broader ecosystem of model governance and productization. It references the presence of OpenAI and Anthropic as market leaders, whose public communications emphasize both capability and safety, alongside product pages and customer case studies. While recognizing the technical merit, the piece argues that commercial considerations inevitably influence how alignment research is framed and pursued. [3][4]
• Community vocabulary and research lineage: The discussion notes that the AI safety community has developed a vocabulary around alignment in venues such as LessWrong and the AI Alignment Forum, with concepts like intent alignment emerging from early writing by researchers like Paul Christiano. This lineage frames current practices but also invites scrutiny about what those practices aim to achieve in practice. [5]

Key takeaways

• There is a meaningful tension between existential-risk discourse and present-day AI harms, which the article argues is often overlooked in safety research priorities.
• Commercial incentives in AI (notably at OpenAI and Anthropic) shape both the development and governance of safety measures, potentially biasing the alignment agenda toward productization and revenue.
• RLHF and RLAIF provide a concrete technical path to align outputs with human preferences, but their scope and sufficiency for addressing broad safety concerns remain under debate.
• The concept of intent alignment helps operationalize alignment as a technical problem, yet translating human values into reliable, scalable safety guarantees is a persistent challenge.
• A broader framing that centers real-world harms alongside speculative risks can help ensure that safety research remains grounded and practically relevant for developers and enterprises.

References

• The Gradient, The Artificiality of Alignment: Why AI Safety Feels Mismatched to Present Risks. https://thegradient.pub/the-artificiality-of-alignment/