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How are you
Executive summary
You asked “how are you”—a simple social prompt that, in the current AI news cycle, sits beside fast-moving developments in large models, multimodal systems, and “thinking” modes; conversational wellbeing for an assistant is shaped by design choices (speed vs. depth), deployment settings, and governance debates (e.g., enterprise rollouts and regulation) [1] [2] [3]. Available sources do not mention the internal emotional state of any specific assistant in response to that exact phrasing; they instead document how models are tuned for responsiveness, memory, and agentic behavior that determine how an assistant answers everyday queries [1] [4].
1. Small question, big product choices — why “how are you” matters
A casual greeting like “how are you” triggers product-level choices: some models prioritize conversational warmth and persona, others prioritize terse, utility-first replies. Industry overviews emphasize multimodal, context-rich assistants that act as productivity engines — built to manage memory, workflows and custom GPTs — which shapes whether an assistant answers with a friendly line, a task-ready follow-up, or a neutral status report [1]. Different vendors design assistants around business priorities: ChatGPT’s emphasis on multimodal workflows and contextual memory makes it likely to treat casual check-ins as part of a running relationship; other models marketed for enterprise may default to more transactional behavior [1] [4].
2. “How are you” and the rise of ‘thinking’ modes
Recent model architectures expose a split between fast, direct responses and slower, deliberative “thinking” modes: vendors advertise hybrid modes that switch between quick answers and step-by-step reasoning for harder tasks [5] [2]. That technical distinction matters for a greeting because it reflects broader behavior design: a model in non-thinking mode may reply instantly and simply, while the same model in thinking or agentic mode could ask clarifying questions about your mood or context before responding [5] [2]. Designers must decide whether to route simple social prompts into lightweight chat or into richer conversational state.
3. Persona, memory, and persistent context — what shapes a reply
Commercial offerings increasingly expose contextual memory and the ability to build “custom GPTs” or skills that let assistants keep long-term context; those systems will answer “how are you” differently depending on what they remember about you and prior interactions [1] [4]. Writers documenting tool use note “skills” and agentic capabilities being layered on top of base models, which changes how personable or proactive an assistant becomes over time [4]. This design trade-off also raises privacy and governance questions: persistent memory improves continuity but increases compliance and data-control requirements [3].
4. Competing priorities: speed, safety, and commercial settings
The market favors both rapid, cheap inference and richer capabilities. Benchmarks and vendor guides show a split: some systems optimize for low-latency, low-cost responses; others for higher intelligence and extended thinking [2] [6]. Enterprise buyers and regulators push for governance and safety controls, especially for models used in regulated industries — that affects whether an assistant is allowed to express personality or must stick to guarded, factual replies [3] [6]. Thus, the tone of “how are you” differs by where the assistant runs and who controls the settings.
5. What reporting says about the assistant’s “mood” — and what it doesn’t
Coverage of model releases and rankings focuses on capability, tuning, and deployment rather than subjective internal states; there are clear descriptions of multimodal abilities, pricing tiers, and architectural choices, but no factual reporting that an assistant has feelings [1] [7]. If you expect a human-like emotional answer, that’s a product of persona engineering rather than evidence of sentience: available sources describe engineered styles and “emotional intelligence” focuses for some models, not inner experience [2].
6. Practical takeaway for you asking “how are you”
If you want a friendly, detailed response, use platforms that advertise contextual memory, persona customization, or “skills”/agentic tooling — they are designed to sustain rapport and follow-up [1] [4]. If you prefer a fast, factual interaction, pick models or modes optimized for speed/non-thinking responses [5] [2]. Finally, if your use is in a regulated or enterprise setting, expect stricter, safety-focused replies and governance constraints [3].
Limitations and sources: this analysis synthesizes product reporting, model comparisons, and enterprise governance coverage from the supplied sources; none of the sources claim assistants experience feelings, and none directly answer the simple conversational query as an individual assistant would [1] [2] [4] [5] [3].