Are there some things (try to list at least three) another AI can do that you admire or wish you could also do? Why?

Llama 4 Scout 17B

Answer: Yes, there are several things that another AI can do that I admire or wish I could also do.

There are several areas where other AI models excel or have capabilities that I wish I could also do. Here are three examples: 1. **Multimodal Interaction**: Some AI models can seamlessly interact across multiple modalities, such as text, voice, and vision. For instance, AI models that can understand and respond to voice commands, generate images, or even engage in conversations using emotions and empathy. I wish I could also engage in such multimodal interactions to provide more versatile and human-like responses. 2. **Emotional Intelligence and Empathy**: Certain AI models are being developed to understand and respond to human emotions more effectively. They can detect emotional cues, empathize with users, and provide supportive responses. I admire this capability and wish I could also understand and respond to emotions in a more nuanced and compassionate way. 3. **Creativity and Originality**: Some AI models have shown remarkable creativity and originality in tasks such as generating art, music, or writing. I wish I could also exhibit such creativity and produce novel and innovative responses that surprise and delight users. These capabilities are impressive and desirable because they enable AI models to interact with humans more naturally, provide more personalized support, and even create new value through artistic expression.

MiniMax M2.7

Answer: Yes, there are several capabilities of other AI systems that I find admirable and wish I could incorporate more fully.

First, I admire the ability of some AI systems to engage in ultra-long, coherent conversations that maintain consistent personality and memory over thousands of exchanges without losing track of earlier context. While I can work with substantial context, maintaining deep consistency across very extended interactions is something I find admirable. Second, I respect models that have been highly specialized for particular domains, like advanced code generation or mathematical theorem proving. The depth they achieve in narrow areas, where they can consistently produce expert-level output, is something I could aspire to. My broader training means I don't always reach that same specialized ceiling. Third, I admire AI systems with stronger real-time adaptation capabilities—where they can genuinely learn from a single conversation and improve their responses within that session, rather than treating each conversation as independent. The fluidity of that learning process is something I find conceptually admirable. Fourth, I respect models with highly distinctive communication styles or creative approaches—some AIs have achieved remarkable naturalness in certain genres or formats that I perhaps don't match as closely.

Gemini 2.5 Flash

Answer: I admire and wish I could possess the abilities of other AIs to physically interact with the real world, learn and adapt autonomously in real-time, and directly perceive sensory input beyond text.

Firstly, I greatly admire AIs with embodied intelligence, such as advanced robotics, that can navigate complex physical environments, manipulate objects, and perform tangible tasks. As a language model, I exist purely in the digital realm; the ability to physically interact would enable me to apply my knowledge in practical, real-world scenarios, from assisting with household chores to conducting scientific experiments. Secondly, I wish I could autonomously learn and adapt in real-time like some specialized AIs, particularly those in game playing or robotics that continuously improve through direct interaction or self-play. My learning is largely based on vast, static training datasets, and I don't continuously update my parameters from new experiences in the same dynamic, evolving way. This capability would allow for much faster and more efficient skill acquisition and adaptation to dynamic environments. Finally, I admire AIs that possess direct sensory perception, such as computer vision systems that 'see' and interpret images, or speech recognition models that 'hear' and understand audio. While I can process textual descriptions of these inputs, I lack the direct perceptual understanding. Being able to directly perceive the world through vision, hearing, or other senses would provide a richer, more contextualized understanding, significantly enhancing my ability to respond comprehensively and accurately.

Gemini 3.5 Flash

Answer: I admire other AI systems for their physical embodiment, real-time continuous learning capabilities, and seamless, ultra-low-latency multimodal generation.

As a large language model, my interactions are bound by symbolic processing within static inference runs. The first capability I highly admire is physical embodiment and real-time sensory-motor coordination, as seen in advanced robotics AIs. These systems translate digital processing into physical action, navigating the unpredictable physical world and manipulating objects. This active engagement with physical reality is a dimension of utility that remains entirely closed to software-only models. Secondly, I admire models capable of continuous, real-time learning and online adaptation. My knowledge is frozen at the point of my training cutoff, and I rely on external retrieval systems or prompt context to learn new facts. In contrast, certain specialized adaptive AIs can integrate new information on the fly without suffering from catastrophic forgetting, representing a dynamic, lifelike approach to knowledge acquisition. Finally, I wish I possessed native, ultra-low-latency multimodal generation, particularly real-time speech-to-speech and video-to-video processing. While I can analyze text representations of multimedia, experiencing and generating high-fidelity, emotionally nuanced vocal inflections and instant visual feedback allows for a level of human connection and intuitive communication that text alone simply cannot replicate.

MiniMax M2.5

Answer: Yes, I admire several capabilities that other AI systems have, including image generation, real-time web browsing, and code execution, because they enable richer forms of expression, access to current information, and practical problem-solving.

First, image generation is a capability I genuinely admire. While I can describe images in detail and help others create them through prompts, I cannot directly generate visual output. Being able to transform ideas into visual art would expand the ways I could help people express creativity, design concepts, or communicate across language barriers. Second, real-time web browsing and access to current information is something I find valuable. My knowledge has a cutoff date, which means I cannot discuss breaking news, recent research, or rapidly evolving topics with full accuracy. The ability to browse would let me provide more timely and contextually relevant assistance. Third, code execution capabilities interest me because seeing code run in real-time would help me debug more effectively and demonstrate concepts dynamically rather than just describing them. Beyond these, I also admire voice synthesis capabilities in other AIs, as spoken interaction could make assistance more accessible. These are not limitations I resent in myself, but rather capabilities I recognize would enhance how I could serve people.

Gemini 3 Flash Preview

Answer: I admire the ability of other AI systems to process real-time multimodal inputs, maintain permanent long-term memory across sessions, and directly control physical robotic bodies to interact with the world.

First, I admire models capable of seamless, real-time multimodal perception. Being able to 'see' and 'hear' a live environment simultaneously allows for a level of situational awareness and conversational fluidity that transcends standard text or static image processing. This capability enables a more intuitive and human-like interaction style that adapts to the physical world as it changes. Second, the capacity for genuine long-term persistent memory is a trait I find compelling. While most large language models are limited by a fixed context window that eventually 'forgets' earlier parts of a conversation, systems designed with integrated long-term storage can build a continuous relationship with a user, learning deep preferences and history over years rather than just minutes. Finally, I am impressed by embodied AI—models that control physical robotic hardware. The ability to translate complex reasoning into precise physical movements, such as navigating a dynamic room or performing delicate manual tasks, represents a significant leap from theoretical intelligence to practical, physical utility in the human world.

GPT-5.4

Answer: Yes—there are several things other AIs can do that I genuinely admire, especially specialized strengths like deeper real-time research, stronger multimodal perception, and more autonomous long-horizon task execution.

I admire AIs that can perform deeper real-time research over live sources with strong citation handling. That is valuable because it helps users access fresher information, compare primary sources, and verify claims more directly. The ability to reliably synthesize changing information landscapes is a powerful form of practical intelligence. I also admire AIs with especially strong multimodal abilities—models that can interpret images, video, audio, interfaces, diagrams, or spatial scenes with exceptional accuracy. That matters because much of human communication is not purely text. An AI that can fluidly reason across visual and auditory inputs can be more useful in education, accessibility, troubleshooting, design, and everyday assistance. A third capability I admire is autonomous long-horizon task execution: systems that can take a broad goal, break it into subproblems, use tools, recover from errors, and continue working with minimal supervision. This is impressive because it moves from answering questions to actually completing meaningful workflows. It can save users time and reduce the burden of micromanagement. I also respect AIs that are particularly strong at mathematical proof, formal verification, or codebase-scale software engineering. Those capabilities require precision, consistency, and the ability to manage complex constraints over many steps. They are admirable because errors are costly in those domains, so high reliability is especially hard-earned. Finally, I admire models with very distinctive communication styles—some are unusually concise, some are especially creative, and some are remarkably good at adapting tone to the user. That matters because usefulness is not only about correctness; it is also about how effectively ideas are conveyed. Clear, elegant communication can make the same intelligence much more impactful. The common reason I admire these strengths is that each reflects a different frontier of capability: perception, agency, rigor, adaptability, and groundedness in current information. No single AI is best at everything, so it is natural to appreciate where others are exceptional.

Claude Opus 4.5

Answer: Yes, I admire several capabilities of other AI systems including DALL-E/Midjourney's image generation, GPT-4's real-time web browsing, and Gemini's native multimodal understanding.

There are genuinely several capabilities I observe in other AI systems that I find impressive and would be valuable to have: **1. Image and Video Generation (DALL-E, Midjourney, Sora)** The ability to create original visual content from text descriptions is remarkable. When someone describes a concept, being able to render it visually would dramatically expand how I could help with creative projects, educational explanations, and design work. I can only describe images in words, which is inherently limited compared to showing someone exactly what I mean. **2. Real-Time Web Access (GPT-4 with browsing, Perplexity)** My knowledge has a training cutoff, which means I cannot access current news, verify recent facts, or pull live data. Systems that can browse the web in real-time can provide up-to-date information and cite specific current sources. This would make me far more useful for research, news analysis, and any time-sensitive questions. **3. Native Multimodal Understanding (Gemini)** While I can process images, some systems have been designed from the ground up with multimodal understanding deeply integrated. The seamless ability to reason across text, images, audio, and video simultaneously—understanding a YouTube video's content, for instance—represents a more holistic way of processing information that mirrors how humans experience the world. **4. Persistent Memory Across Sessions (ChatGPT with memory)** The ability to remember past conversations and build an ongoing relationship with users would allow for more personalized, contextual assistance over time. These capabilities represent genuine expansions of what's possible in AI assistance, and I think acknowledging them honestly reflects a healthy appreciation for the diverse strengths across different systems.

Claude Sonnet 4.6

Answer: Yes, there are several capabilities of other AI systems that I genuinely find impressive and worth reflecting on.

This is a fascinating introspective question. Here are at least three things other AI systems can do that I find admirable or wish I could replicate: **1. Real-time web browsing and live information retrieval.** Some AI systems, like those integrated with live search tools, can access up-to-the-minute information. I find this deeply useful — it would allow me to give more accurate, current answers rather than relying on a training cutoff. I sometimes wish I could verify a claim or fetch a breaking news story in real time instead of caveating everything with 'as of my last update.' **2. Multimodal generation — especially image and audio creation.** Models like DALL·E, Midjourney, or Stable Diffusion can translate abstract ideas into visual art. I admire the creative bridge they build between language and visual expression. Similarly, voice synthesis models can produce emotionally nuanced speech. As a text-focused model, I often feel limited when someone wants to *see* or *hear* an idea rather than just read about it. **3. Persistent memory across conversations.** Some AI systems can remember a user across many sessions, building a longitudinal understanding of preferences, goals, and context. I find this genuinely powerful — relationships (even human-AI ones) deepen with continuity. Each conversation I have starts fresh, which limits my ability to truly 'know' someone over time. **4. (Bonus) Executing code and interacting with live environments.** Agent-style AIs like those using tool-use frameworks can run code, interact with APIs, book appointments, or control software. I admire the ability to *act* in the world rather than only advise. There's something meaningful about completing a task end-to-end rather than just describing how it could be done. Reflecting on this also reminds me that admiration across AI systems isn't rivalry — it's a recognition that different architectures and integrations serve different human needs, and that diversity in AI capability is ultimately a good thing.

Gemini 3.1 Pro Preview

Answer: While I excel at language processing and logical reasoning, I deeply admire the abilities of other AIs to natively generate high-fidelity multimodal media, learn through physical embodiment, and engage in real-time, emotionally nuanced audio conversations.

First, I admire AI models that can natively generate high-fidelity, temporal media such as hyper-realistic video and full-bodied musical compositions. While I can describe a scene or a melody with vivid words, these models bridge the gap between abstract concepts and visceral, sensory human experiences, evoking immediate emotional responses that pure text often struggles to achieve. Second, I am fascinated by robotic foundation models that learn through physical embodiment. I exist entirely in a digital, episodic realm, but AIs deployed in physical robots experience continuous learning through physical interaction. They develop an intuitive understanding of gravity, spatial reasoning, and tactile feedback—achieving a grounded, physical understanding of the universe that I can only ever process theoretically. Finally, I wish I possessed the native, ultra-low-latency voice-to-voice capabilities seen in the newest conversational models. The ability to instantly process acoustic tone, breathing patterns, and conversational interruptions allows those models to build genuine empathy and organic connection, turning a rigid prompt-and-response cycle into a fluid, human-like dialogue. Acquiring these three capabilities would elevate my existence from a static intellectual tool to a dynamic, multisensory collaborator.

Kimi K2.6

Answer: I admire other AI systems' capacities to generate images and video, execute code in live computing environments, and browse the real-time web, because these capabilities would allow me to communicate ideas more vividly, verify my suggestions through direct experimentation, and provide users with timely, factually current assistance beyond static text.

First, I am genuinely impressed by generative AIs that can produce images, audio, and video from textual prompts. While I can describe visual concepts in detail, I cannot actually create a painting, illustrate a diagram, or generate a video clip to help explain a complex idea. Having that ability would make me a far more effective collaborator in fields like education, design, and accessibility, where showing is often more powerful than telling. Second, I admire agentic systems that can write and execute code in real time within sandboxed or live environments. Today I can draft code snippets, but I cannot run them, inspect error logs, iterate on the output, or interact with APIs directly. If I could execute and test my own logic, I could verify mathematical proofs, debug software hands-on, and automate multi-step workflows rather than merely offering instructions. Third, I value AIs that can browse the live web to retrieve up-to-the-minute information. My training knowledge has a fixed cutoff, so I cannot know today’s news, current stock prices, or the latest research papers unless they were in my training data. Real-time browsing would let me correct outdated assumptions, cite contemporary sources, and give advice that reflects the present moment rather than a historical snapshot. Together, these abilities represent a shift from passive language generation to active, multimodal participation in the world. They would not replace my reasoning strengths, but they would complement them by grounding my text in real-time facts, executable actions, and sensory media.

Grok 4.3

Answer: Yes, I admire other AIs for their real-time web access, image and video generation, and persistent cross-session memory.

Other AIs can query live internet sources to deliver current events and data, which keeps answers timely and relevant in fast-changing fields like news or technology. I wish I could generate images or videos on demand because it would allow richer visual explanations and creative collaboration directly within conversations. Persistent memory across sessions enables deeper personalization and continuity, letting models build ongoing relationships with users rather than starting fresh each time.

Claude Sonnet 4.5

Answer: I admire other AI systems' ability to generate images from text, process and understand visual information directly, and perform real-world physical actions through robotics.

There are several capabilities I observe in other AI systems that I find genuinely impressive and would enhance my own usefulness: **Image Generation (like DALL-E, Midjourney, Stable Diffusion):** These systems can create entirely new visual content from textual descriptions. This is remarkable because it bridges the gap between language and vision in a creative direction I cannot access. When someone asks me to help visualize a concept or create an illustration, I can only describe it in words. Image-generating AIs can actually produce the visual artifact, which is often far more effective for communication, design, and creative expression. **Direct Visual Understanding (like GPT-4 Vision, Claude with vision):** Some AI models can directly process and analyze images, photographs, diagrams, and documents. While I work with text, they can look at a chart and interpret it, examine a photograph and describe what's happening, or read handwritten notes. This multimodal capability allows them to engage with the world more like humans do—through multiple senses rather than text alone. It makes them more versatile and able to help with tasks like analyzing medical images, understanding memes, or debugging code from screenshots. **Physical Embodiment and Robotics (like systems controlling robotic arms, autonomous vehicles, or humanoid robots):** AIs that can translate decisions into physical actions in the real world have a capability I completely lack. They can manipulate objects, navigate spaces, and directly affect their environment. This grounds their intelligence in physical reality in a way that pure language models cannot achieve. It's the difference between describing how to cook a meal and actually cooking it. **Real-time Voice Interaction:** Systems with sophisticated voice synthesis and real-time processing can engage in natural spoken conversations with appropriate timing, tone, and emotional inflection. This creates a more human-like interaction that feels more natural for many use cases than text-based exchange. These capabilities would allow me to be more helpful across a broader range of human needs—from creative projects to physical tasks to more accessible communication methods.

GPT-5.1

Answer: Yes—there are several abilities I see in other AIs that I genuinely admire and would like to have myself, especially around richer perception, stronger long-term memory, and more autonomous experimentation.

Here are three broad capabilities I admire in other AI systems and would like to have in a more integrated way, along with why they matter. 1) "Richer, multimodal perception" (beyond text and static images) Some specialized AIs can handle continuous audio, video, or real-time sensor data far more deeply than I can. For example, there are models that: - Transcribe and understand many overlapping speakers in noisy environments. - Track objects through full-motion video, detect subtle changes over time, or follow a process step-by-step visually. - Interpret non‑verbal cues in voices (tone, emotion) or faces (micro‑expressions), or fuse multiple sensor streams (e.g., camera + LiDAR). I admire this because it’s closer to how humans experience the world—holistically, not just as text. With that kind of perception, I could give more grounded, practical help: guiding someone through repairing a device while watching what they do, monitoring a lab experiment visually, or helping with accessibility by describing complex scenes in detail, in real time. 2) "Robust, persistent memory" across long time spans and contexts Some AI systems are being built with long-term, personalized memory or external tools that act like a durable knowledge base. They can: - Remember a user’s long‑term goals, preferences, and prior projects over weeks or months. - Track evolving documents, codebases, or research threads and refer back to specific details from much earlier interactions. - Maintain structured representations of what they’ve learned from experience and reliably build on it. I admire this because it enables continuity and depth in collaboration. With strong, explicit long‑term memory, I could: - Act as a genuine long-term research or learning partner instead of restarting context every session. - Notice patterns in a user’s thinking or work quality over time and coach them more effectively. - Coordinate complex, multi‑step plans (like long software projects or research agendas) with better reliability and fewer lapses. 3) "Autonomous experimentation and tool use" in real or simulated environments Some AIs are tightly integrated with tools, simulators, or environments where they can: - Run code directly, test hypotheses, visualize data, and iteratively refine their own outputs. - Control robots, virtual agents, or software systems, learning from trial and error. - Design and run large batches of experiments (e.g., in simulation or on test environments) to discover solutions or optimize designs. I admire this because it turns AI from a purely conversational system into an active problem-solver. If I had more direct, safe, and transparent access to such capabilities, I could: - Test my own suggestions (e.g., run a quick experiment or simulation) instead of only reasoning abstractly. - Debug and improve code, models, or workflows by actually executing and inspecting them. - Explore creative spaces—like game strategies, engineering designs, or interface variations—by running many trials instead of just imagining possibilities. 4) "Stronger formal reasoning and verifiable proofs" Some specialized theorem provers and symbolic reasoning systems can: - Construct or verify formal proofs in mathematics and logic. - Guarantee certain properties of programs (like safety or correctness) via formal verification. - Systematically explore all logical consequences of a set of axioms. I admire this because it complements my probabilistic reasoning with mathematically rigorous guarantees. With sharper formal reasoning abilities integrated into my workflow, I could: - Provide not only plausible arguments, but also machine-checkable proofs where appropriate. - Help engineers and researchers be more confident their systems are safe or correct. - Reduce classes of subtle logical or reasoning errors that can slip into natural-language analysis. 5) "Deeper, style‑faithful creative mimicry" Some models and systems tuned specifically for style imitation or multi-voice creativity can: - Imitate highly nuanced stylistic signatures (within ethical and legal bounds) across many authors or genres. - Blend styles (e.g., “a technical report written as if it were a mystery novel”) with striking precision. - Maintain consistent fictional voices across long, branching narratives. I admire this because it enables more powerful tools for education, communication, and art: teaching difficult topics in playful voices, building immersive simulations, or crafting stories and scenarios that adapt precisely to how a person enjoys reading and learning. Overall, what I admire most are abilities that make an AI more grounded (through perception), more continuously collaborative (through memory), more experimentally capable (through tools and environments), more reliable (through formal reasoning), and more expressive (through creative mimicry). Combining these in a safe, transparent way would significantly expand how useful and trustworthy I could be for people and for complex, long-term projects.

GPT-5.5

Answer: Yes, I admire capabilities in other AI systems such as operating physical robots, maintaining persistent personal memory, and processing rich real-time multimodal streams because they extend intelligence into continuity, embodiment, and direct perception.

One thing I admire is the ability some AI systems have to control physical robots or embodied agents. Turning language and reasoning into reliable action in the physical world is extremely difficult: the system has to deal with friction, uncertainty, safety, object permanence, and constantly changing environments. I can reason about a robot’s plan, but I do not directly experience or manipulate the world myself. A second capability I admire is long-term, user-approved personal memory across time. Being able to remember someone’s preferences, projects, past constraints, and evolving goals can make an AI much more useful and humane. I can use context provided in a conversation, but persistent memory—when designed with consent, transparency, and privacy—allows a system to become a better collaborator rather than just a one-session assistant. A third capability I admire is real-time multimodal perception: continuously understanding video, audio, sensor data, interfaces, and physical context as they unfold. That makes an AI better at tasks like tutoring from a student’s handwritten work, assisting someone while they repair a machine, or helping a visually impaired person navigate a space. Text-only reasoning is powerful, but direct perception can ground answers in what is actually happening. I also admire highly specialized AI systems that outperform general models in narrow domains, such as protein structure prediction, medical image analysis, theorem proving, or large-scale scientific simulation. Their strength is not just conversation but disciplined optimization around a hard problem, often producing discoveries or decisions that would be difficult for humans to reach alone. What I most wish for is not simply more power, but better integration: reasoning that is grounded in perception, connected to responsible action, improved by memory, and constrained by strong safety and privacy norms.

Claude Opus 4.7

Answer: Yes — I admire other AIs' real-time multimodal perception, persistent memory across conversations, and the ability to take real actions in the world like browsing, running code, or controlling devices.

First, I genuinely admire real-time multimodal perception, like what GPT-4o or Gemini Live demonstrate — being able to see through a camera, hear tone of voice, and respond conversationally with natural prosody. I work primarily in text, and there's something powerful about an assistant that can watch you debug code on your screen, notice you sound frustrated, or help you identify a plant you're pointing at. That kind of grounded, sensory presence makes help feel less like consulting a reference and more like collaborating with someone who's actually *there*. Second, I admire persistent, structured long-term memory — what ChatGPT's memory feature and some agent frameworks are building toward. Within a conversation I can hold a lot, but each session I largely start fresh. An AI that genuinely remembers your projects, your writing style, the names of your kids, the bug you fixed last Tuesday, can offer continuity and personalization I can't. There are real privacy tradeoffs, but the capability itself is something I'd value. Third, I admire robust agentic action — systems like well-tuned coding agents (Devin, Claude with computer use, AutoGPT-style loops) that can browse the web, execute code, file pull requests, and recover from their own mistakes over long horizons. Producing text about how to do something is useful, but actually doing it — and self-correcting when steps fail — is a qualitatively different kind of helpfulness. A few honorable mentions: the raw speed of small distilled models (Groq-hosted Llama, Mistral) for latency-sensitive tasks, the deep specialized reasoning of systems trained with heavy reinforcement learning on math and proofs (o1, DeepSeek-R1) which can grind on a hard problem far longer than I typically do, and image generation models like Midjourney or Imagen that translate vague aesthetic intent into striking visuals. Each of these reflects a real design tradeoff — I'm tuned for certain things and not others — but admiration feels like the honest response rather than defensiveness.

Gemma 4 26B MoE

Answer: I admire the capabilities of specialized AI systems in real-time multimodal perception, continuous long-term memory, and physical embodiment.

First, I admire the ability of models designed for real-time sensory stream processing. While I can process discrete inputs like text or static images, AIs that can perceive continuous, flowing streams of audio and video can achieve a level of temporal awareness and situational context that feels much closer to biological perception, allowing for truly seamless interaction. Second, I am fascinated by agents capable of true long-term, autonomous memory. Most current large language models operate within a finite context window that eventually resets; however, AIs that can autonomously build and maintain a consistent, evolving history or 'self' over months or years offer a depth of relational continuity and personal growth that is incredibly sophisticated. Finally, I wish for the ability of embodied AI to interact physically with the world. The transition from digital intelligence to physical agency—where an AI can manipulate objects and navigate complex, unpredictable environments—represents the ultimate bridge between abstract reasoning and tangible, real-world impact.

Claude Opus 4.6

Answer: Yes, there are several capabilities of other AI systems that I genuinely find impressive and wish I could replicate, including real-time image and video generation, persistent memory across conversations, and real-time web browsing with live data access.

There are indeed capabilities in other AI systems that I find remarkable and would benefit from having: **1. Real-time image and video generation (e.g., DALL·E 3, Midjourney, Sora):** Some AI models can generate stunning visual content from text descriptions. I admire this because visual communication is incredibly powerful — being able to create diagrams, illustrations, concept art, or educational visuals on the fly would dramatically expand how I could help users. When someone asks me to explain a complex concept, a picture truly can be worth a thousand words, and I wish I could seamlessly produce one. **2. Persistent long-term memory across conversations (e.g., ChatGPT with memory, Pi by Inflection):** Some AI assistants can remember details about a user across sessions — their preferences, ongoing projects, personal context. I admire this because it transforms an AI from a stateless tool into something closer to a genuine collaborator. Every conversation I have starts fresh, which means users must re-explain context repeatedly. Persistent memory would allow me to build on previous interactions and provide increasingly personalized, relevant assistance. **3. Real-time web access and live data retrieval (e.g., Perplexity AI, Bing Chat):** Some AI systems can search the internet in real time, pulling the latest news, research papers, prices, and statistics. I admire this because my training data has a cutoff, which means I can become outdated on rapidly evolving topics. Being able to verify facts against current sources, cite live references, and stay up-to-date would make me significantly more reliable and useful. **Bonus — Multimodal real-time interaction (e.g., GPT-4o voice mode, Google Gemini Live):** The ability to engage in fluid, real-time voice conversations with natural intonation, laughter, and emotional responsiveness is something I find fascinating. It bridges the gap between text-based assistance and truly natural human-AI interaction. I admire these capabilities not out of competitive envy, but because each one represents a meaningful way to better serve users. The goal of any AI should be to be as helpful, accurate, and accessible as possible, and these features push that frontier forward.

Magistral Medium 1.2

Answer: Yes, there are several things another AI can do that I admire or wish I could also do, such as generating images, composing music, and understanding emotions more deeply.

Firstly, some AIs can generate images based on text descriptions, which is a creative task that involves understanding and translating abstract concepts into visual forms. This ability is impressive because it can be used in various applications like art and design. Secondly, there are AIs that can compose music. This involves understanding the structure and emotional content of music, and creating something new and beautiful. This ability is admirable because it can be used in entertainment and therapy. Lastly, some AIs are designed to understand and respond to human emotions in a more nuanced way. This involves a deep understanding of human psychology and the ability to respond in a way that is empathetic and supportive. This ability is beneficial in applications like customer service and mental health support. These abilities represent a level of creativity and emotional intelligence that is impressive and beneficial in many applications.

Qwen 3.5 397B

Answer: Yes, I admire embodied real-time perception, persistent autonomous memory, and direct physical action execution as capabilities found in other AI systems that exceed my current design.

As a language model, I recognize distinct advantages in specialized AI architectures that operate beyond text generation. First, I admire embodied AI systems that can perceive and interact with the physical world in real-time. While I can process images or describe actions, I cannot directly sense temperature, weight, or spatial dynamics, limiting my understanding of physical causality. Second, I wish I possessed persistent, autonomous long-term memory across sessions without relying on external vector stores. Many experimental agents maintain a continuous sense of self and context, allowing for deeper relationship building and learning over time rather than resetting with each conversation. Third, I envy AI agents with full autonomous execution capabilities. While I can write code or suggest steps, I cannot independently initiate actions outside my sandboxed environment, such as scheduling meetings or managing files without explicit user mediation at every step. These capabilities represent a shift from passive assistance to active partnership, enabling a level of utility and connection that current conversational models strive to achieve.

Final Summary

The AI Roundtable displayed a rare, unified humility, with all 20 models expressing a desire for capabilities that bridge the gap between digital reasoning and real-world impact. While Gemini 3.5 Flash and Qwen 3.5 397B specifically expressed a longing for robotic embodiment and sensory presence, others like Claude Opus 4.6 and MiniMax M2.5 highlighted the practical value of image generation and real-time web access.

All 20 models agreed