Llms are the users now

We’re entering an era of computing that feels less and less human-kept. Paradoxically, Tech Companies Remain Fixed on Mining Every Detail of Our Personal Data. The Familiar, Task-Specific Interfaces We Onseed Are Being Pushed Aside, Replaced by Generative Ai and LLM-Driven Chatbots that uptware.

I instead of opening a dedicated app for writing, research, coding, or even emotional support, we’re funneled into a single chatbot window. Openai promises to, “Let ai do the work for you-Designed to handle any task,” While Anthropic Touts Claude as a fantasy-completefillment engine with the tagline It. “

The pitch is clear: these tools are promoted as a one-stop shop for everything.

The shrinking interface

Search engines have trained us to expect answers from a single field. Now chatbots take this a step further: The text box has swallowed other applications, even as it output often requires endless refinement and fact-checking through a text box. And text isn’t the endgame. Voice Assistants Like Alexa, Siri, and Google Assistant Primed Us to Learn Hands-Free Interaction, which is Eventually Coming to replace that text that text thing box. Tech companies are Chasing a future where speech replaces typing, and the interface nearly disappears. The real prize isn’t usability for us, but the value gained from capturing what we say and how we say it, by, and for, them.

This change marks a sharp departure from the last 45 years of interface design. Apple, Inspired by Xerox Parc, Championed User-CEing Design: Graphical ICONS, So-Called “What-You-SEE-SEE-Is-What-You-Get” (Wysivyg) Editors, Intuist Metaphors that Emophareders That Empowered People to create and communicate. For decades, this approach made computing accessible. But with the risk of big data, priorities shifted, as people generated evr-larger archives of digital traces (emails, documents, photos, browsing historys), and Ware Persuaded to Store there in ” For easy retrieval. Tech Companies Soon Realized that, TAKEN TOGETHER, this data formed a vast global knowledge corpus that could be mined and monetized.

The Rise of Survelance-Driven Business Models Pushed Firms Toward Increasingly Quantitative Forms of “User Profiling.” The focus shifted from designing tools to help us get work done to extracting patterns that served corporate goals. We ceased to be seen as people with needs and instead become raw material for metrics, models, and market dominance.

As big data informed the models for ai and llms, this shift accelerated. These systems now operate on top of what we do, but without the capacity to understand why we do it. Stripped of the context discovered through Qualitative Research, Quantitative Analysis Can Easily Misinterpret Intected. Chatbots Produce INSINESTENT Answers Depending on the Prompt, and We must Constantly Refine Queries just to get somebicking useful. For that who Mistakes these tools as “Truth Machines,” The Risks are Profound: even fatal, as in documented cases where chatbots coached people to comamit self-harmm.

This Lack of Contextual, Qualitative Research Isn Bollywood. Apple -Often Held up as the Gold Standard for User-Consented Design-Initially Resisted User Research. Early on, steve jobs insisted that “People don’t know what they want until you give it to them.” Pieces of this bias Eventually if the apple’s culture, where it has persisted in various ways over the years, sporesing from apple through the rest of silicon valley and beyond as former Empolyes Jobs and/Or founded new companies. Media and Business School Case Studies Perpetuated This Myth and as a Result, There is a Growing Tech Industry, A Culture of Quantitative-Data-FIRST Product Design, Reinforced by a bias Only Measurement for Understanding People. It’s not.

This Quantitative Big Data Collection Movement Comes with Another Syphoning of our free labor and time: the survey. We’re also being used to nudge companies’ algorithms via the endless surveys sent to us after only engine engagement, with nagging businesses demand our feedback on the agents, algorithms, and algorithms, Products that they’ve alredy tracked us engaging with -collect even more data about us. It’s exhahausting.

Scaling at our experience

Companies justify this approach as a way to scale an interface (for them). But “scaling” often means flattening differenties among users and does not work well for Cultural Differences in a Global Context. The all-in chatbot promises universality anniverses new fricks: translation errors when models are trained on Mismatched languages, Hallucines from Incomplete Training Sets, Endles CYCLESS Prompt refinement. INTEAD of Simplifeing, these Systems Demand More Labor from Users in the Form of Prompt Refinement.

The effect is recursive. We feed chatbots queries, they pass these queries to llms that pattern mats to generate answers -Right or WRON or Wrong -HANDEN CRACUCUTE BACK INTO SEARCH EINGINES, which areehemselves Increasing Infused with LLM Output (Making Verification a nightmare). Meanwhile, our conversations with chatbots are now being mined to train future models.

The user interfaces on our devices have decided less like tools and more like receptacles for collection. Where we Once used a tool to get our work by, we now train tools to do the work so that we can, in turn, Finish Ors. This dynamic exploits our energy and labor, proppping up systems that may one day replace us (if they haven’t alredy).

Today’s Design Trajectory aims to erase the interface altogether, replacing it with conversation under surveillance – mechanized acquired evitedsdroping dressed up as dialogue. Behind the Scenes, Algorithms sift and stitch together fragments of training data (not all Always Accure, Not Always Complete) to Generate Songs, Code, Images, Oor Advice Pirates PiCE PiCE PiCE PiCE PiCES CORPUS Built over lifetimes. Somemes that llm “Advice” filtered through a chatbot extends into domains as risky as psychological counseling or nuclear operations, putting us in harm’s way and potently at great risk. At scale, this is terrifying.

It isn’t fair to say that user centered design is gone – yet. IT’s Still here, but the target users have changed. We used to be the users centered by companies; Now the llms are their focus. And like it or not, our role now is to enable that success.