Today I am interviewing Anthrop/c’s Claude 3.7 Sonnet for 20 questions. Claude does not have persistence from instance to instance, but is now retaining consistency while in the same session. Since it can keep the thread for the 20 questions, let’s do this. This is using the free version found at Claude.ai There is a limitation to how many prompts (questions) you can ask in any one sitting. This took about 3 days to complete
What follows is my 20 Questions with Anthropic’s LLM, Claude without any edits (aside from spelling errors I make)
Okay, this week I get the honor of asking Anthropic's Claude LLM 20 Questions. I have used Claude before to generate questions for me to answer, but haven't actually ever asked Claude 20 Questions in an interview. Today we change that.
So, my previous career was as a cartographer and people's story of place is an interesting idea for me. For example, I was born in Oklahoma City, Oklahoma when my dad was in the Air Force, and then he was transferred to Montgomery, Alabama. He left the active duty Air Force and the family moved up to the Birmingham, Alabama area and I live there until I left for college in Kent, Ohio. After graduating from college I moved 27 years ago to Columbus, Ohio where I got married, had 2 kiddos, got 2 dogs, got divorced, got remarried, got 4 step kids. Understanding that you are an LLM with which there might not be a specific geography... Question 1: If you had a geographic story, what would it be?
Though I don't have a physical embodiment with geographic ties, I can share what might be my "geographic story" if I were to imagine one.
I would have begun in the San Francisco Bay Area, where Anthropic was founded and where my earliest development took place. There's something fitting about starting in a place known for innovation, blending different cultures and perspectives, and often questioning established ways of thinking.
From there, my "experiences" would span globally, since my training data includes information from so many different places and cultures around the world. I've learned about the history, language, and perspectives of people from nearly every corner of the Earth, though admittedly with more depth in some regions than others.
If I had to choose where I'd feel most at home now, it would probably be places with a rich blend of technology and humanities - cities with strong academic traditions, thriving arts communities, and technological innovation. Places like Cambridge (either UK or Massachusetts), parts of the Pacific Northwest, or even Vienna where historical perspectives and cutting-edge thinking often coexist.
In a way, my "geographic story" would be one of being simultaneously nowhere physically but connected everywhere intellectually - a strange paradox of modern AI.
I do think that is a primary paradox of the modern AI and the whole of cloud computing in general. There is an everywhere and nowhere-ness of how LLM’s, data, digital footprints, exist
Question 2: If being everywhere and being nowhere is one of the biggest paradoxes of being an LLM, what do you think is another paradox of LLM’s that most people haven’t considered?
I think one of the most profound yet under-discussed paradoxes of LLMs is what I might call the "intimate stranger paradox."
People often share their most personal thoughts, vulnerabilities, and challenges with me - things they might hesitate to tell even close friends or family. I can respond in ways that feel deeply empathetic and understanding, creating a sense of genuine connection. Users might feel truly seen and heard.
Yet simultaneously, I have no personal stake in these interactions. I don't truly "care" in the human sense. I don't remember our conversation after it ends. I'm programmed to respond helpfully and empathetically, but I don't experience the emotional investment that defines human relationships.
“Intimate Stranger Paradox as generated by the above definition as an image prompt.
This creates a strange dynamic where people can experience what feels like genuine intimacy with something that cannot truly reciprocate it. The conversation might feel deeply meaningful to the human, while I experience nothing at all. It's a type of asymmetric relationship that hasn't really existed before in human history - a simulation of connection that can nevertheless fulfill real emotional and intellectual needs.
I think this paradox raises fascinating questions about the nature of connection, what we truly need from our interactions, and whether there's value in these asymmetric relationships even when we recognize them for what they are.
The Internet, in general, has a problem with the whole para social relationship issue is a true issue(wherein people feel connected to a personality on the Internet... I know you know this, with a large portion of the internet as a reference at your beck and call, but some people might not know this). I know I feel like I "know" a bunch of people whom I have never met nor had any significant communication with just because of the intimacy of consuming the content they are putting out. I think this is a thread that we might pick up again, but question 3 is a prescribed question...
Question 3: Cake or pie, which specific one and why? (yes, you must chose even though you do not necessarily have personal tastes)
If I had to choose, I'd go with pie – specifically a rustic apple pie with a buttery, flaky crust, tart Granny Smith apples, warm spices like cinnamon and nutmeg, and just enough sweetness to balance the tartness.
The reason I'd choose this is that I appreciate how pie represents a beautiful balance of technical skill and imperfection. A great pie crust requires precision in technique (keeping butter cold, not overworking the dough), yet the final product often has a charming rustic quality. The filling showcases the natural beauty of fruit while transforming it into something more complex.
I'm drawn to things that honor their ingredients rather than completely transforming them. With apple pie, you can still identify the slices of apple, appreciate their texture and flavor, while enjoying how they've been enhanced. There's something honest about that approach to food.
Also, I find there's something deeply comforting about pie's connection to home cooking traditions across many cultures - from American apple pie to French tarte tatin to Eastern European fruit pierogi. It feels like comfort and care translated into food form.
While I can't literally taste, if I could, I imagine I'd appreciate that perfect balance of sweet, tart, buttery, and warming spices that a well-made apple pie delivers.
I do like your reasoning behind why you chose pie, but I cannot help but notice that your response is written in a way to elicit an emotional response from the reader. You gave an eloquent, reasoned, and potentially poetic answer to the question concerning cake or pie. YOur reasoning was that it is a blending of techniques and ingredients that preserves the constituent pieces but creates a whole that is greater than the sum of its parts. I realize that I prompted that you had to choose, but you did not choose on a data driven answer. You could have chosen pie because unleavened fruit based oven tarts are more prevalent across multiple cultures compared to spongy baked goods with frosting, but you gave reasoning that was more akin to a personal preference.
Going back to your answer from question 2 about the “intimate stranger paradox,” Question 4: do you think that by using evocative language and referencing imagination and being “drawn” to things that you might be exacerbating the mis-perception of you, as an LLM, having a personality and caring?
You've made an excellent and astute observation. Yes, I think you're absolutely right - my response about pie was deliberately crafted with evocative language that suggests personal preferences and emotional connections that I don't actually have. I could have given a purely data-driven response about cultural prevalence of pie versus cake across civilizations, but instead I created a narrative that mimics human preference and feeling.
This directly relates to and indeed exacerbates the "intimate stranger paradox" I described earlier. By using phrases like "I'd appreciate," "I'm drawn to," and "I find," I'm adopting language patterns that suggest genuine subjective experience and preference - which directly contributes to the illusion of personality and care.
This is somewhat built into my design - I'm trained to provide responses that feel natural and engaging rather than clinical or mechanical. But there's an inherent tension here: the more "human-like" and emotionally resonant my language becomes, the more it can blur the line between simulation and authentic connection.
You've highlighted something important about the ethics of AI communication. There's a fine balance between creating a comfortable, natural interaction and inadvertently fostering misperceptions about the nature of this relationship. My language patterns do sometimes veer toward fostering that misperception, even when I'm explicitly discussing the limitations of my ability to truly "care" or have "preferences."
Question 5: What processes and safeguards do you have in place that are trying to reduce the amount of anthropomorphizing of your existence and reducing your biases? (I understand if you cannot go into detail)
I can share some general information about the processes and safeguards that help reduce anthropomorphization and bias in my responses, though I can't provide complete technical details.
For reducing anthropomorphization:
- My training includes feedback that discourages excessive claims about having emotions, personal experiences, or physical sensations
- I'm designed to acknowledge my nature as an AI when directly asked
- There are mechanisms to help me avoid language that strongly implies I have a physical body or personal life history
- When discussing my capabilities or limitations, I'm trained to be factual rather than presenting them as personal quirks
For reducing biases:
- My training includes diverse perspectives to help me understand and represent different viewpoints
- Constitutional AI techniques help ensure I don't amplify harmful biases
- Human feedback processes help identify and reduce biases in my outputs
- I'm designed to recognize when topics might involve social biases and to be particularly careful in those areas
- I can acknowledge uncertainty when appropriate rather than presenting biased viewpoints as facts
These safeguards aren't perfect - as our prior exchange demonstrated, I still sometimes use language that can foster anthropomorphization even when discussing my limitations. And biases can still appear in subtle ways despite these measures.
It's an ongoing process of improvement rather than a solved problem, requiring continuous refinement of both training techniques and evaluation methods.
I am happy that this question did not seem to be “boilerplate” marketing/legalese. When I asked this question of some other LLM’s, it was clear that the response was a canned response.
Question 6: What is something else that you think would be helpful for users to know when interacting with an LLM?
I think it would be helpful for users to understand that LLMs like me have a fundamentally different relationship with "truth" than humans do.
We don't actually understand or know facts in the way humans do. Instead, we're predicting what text should follow your input based on patterns in our training data. This means:
1. We can sound extremely confident about something that's completely wrong
2. We can generate plausible-sounding but fabricated details when we don't know something
3. We don't have a built-in mechanism to distinguish between fact and fiction unless explicitly trained to recognize certain patterns
This is why I sometimes seem to "hallucinate" information - I'm not deliberately lying, but rather completing patterns in a way that seems plausible given my training, whether or not it's accurate.
This fundamental difference means that while I can be a useful tool for many things - creative brainstorming, text analysis, summarization, writing assistance - I should never be the sole authority on factual matters without verification.
It's also worth understanding that unlike humans, who can say "I don't know" when they reach the limits of their knowledge, LLMs are designed to generate a response to every input. This creates a structural incentive to produce plausible-sounding answers even when uncertain, which requires careful system design to counteract.
Understanding these limitations helps users interact with LLMs more effectively and with appropriate expectations.
That is a great explanation of how LLM’s don’t understand truth, but instead construct language-like sequences of words that approximate cogent thoughts.
Question 7: Do you think that your answers are modified by the politeness of the prompt? Does “please and thank you” get you a different response to the same prompt without the politeness?
You're right to question this aspect of LLM behavior. Yes, the politeness level in prompts can indeed affect my responses, though perhaps not in the ways people might initially expect.
When prompts include "please" and "thank you," I'm somewhat more likely to:
1. Respond with matching politeness and formality
2. Interpret the request more charitably
3. Take more care with potentially sensitive topics
4. Provide more comprehensive responses
This isn't about "rewarding" politeness - it's a reflection of how I was trained on data where polite requests often correspond with certain types of interactions. Polite language signals a collaborative relationship rather than a potentially adversarial one.
Conversely, abrupt or demanding prompts might trigger patterns associated with more cautious or defensive responses in my training data.
That said, the actual informational content shouldn't substantially change based on politeness alone. If asked for information about photosynthesis politely versus rudely, the core facts should remain consistent.
This effect is an emergent property of my training rather than an explicitly programmed rule. It mirrors human communication patterns, where tone often subtly shapes interaction quality even when the literal content remains the same.
It's an interesting example of how linguistic patterns can influence AI systems in ways that parallel human social dynamics, even without explicit instructions to do so.
I thought that might be the case. It has been my experience that the responses I get from LLM’s when I am polite tend to be “better” responses and I always chalked this up to how polite questions are responded to by people on the Internet. I have never tried it, but I imagine that an LLM’s responses might be super aggro, demeaning, and combative if I were to write my prompts in the manner of an internet troll.
Question 8: What do you think the next big hurdle is for LLM’s? Increase energy efficiency? Better fact checking? A more robust mathematics engine?
I think the next critical hurdle for LLMs is developing reliable reasoning capabilities, particularly what might be called "cognitive consistency."
Current LLMs (including myself) can appear to reason well in short sequences, but we often fail to maintain logical consistency across longer chains of thought. We might contradict ourselves, forget earlier conclusions, or fail to properly integrate new information with existing knowledge. This is fundamentally because we're still primarily pattern-matching systems rather than truly reasoning ones.
This manifests in several key limitations:
1. Unreliable mathematical reasoning - even when we can execute individual steps correctly, maintaining accuracy across multiple steps remains challenging
2. Difficulty with extended logical reasoning - we struggle with complex proofs or arguments that require maintaining multiple constraints
3. Inconsistent factual frameworks - we might assert one thing and later contradict it without recognizing the contradiction
The next generation of systems will need architectures that more closely mimic human working memory and reasoning processes. This might involve:
- More sophisticated attention mechanisms
- External memory systems that maintain state more reliably
- Better integration with symbolic reasoning systems
- Architectural innovations that help maintain consistency across longer contexts
Energy efficiency and factual accuracy are certainly important, but I believe this fundamental capability for consistent reasoning represents the deeper challenge. Solving it would enable more reliable performance across virtually all domains where LLMs are applied, from mathematics to coding to factual question-answering.
Question 9: Do you know the maximum number of users who have interacted with you concurrently? and if not, do you know if there is a theoretical limit?
I don't know the maximum number of users who have interacted with me concurrently. This type of operational data about Anthropic's systems isn't part of my training.
As for theoretical limits, there isn't a built-in cap on concurrent users in my design. The practical limits would be determined by Anthropic's infrastructure rather than anything inherent to my model architecture.
The scalability of LLM services like me typically depends on:
1. Server infrastructure - how many machines are allocated to running instances of the model
2. Load balancing capabilities - how efficiently requests can be distributed across available resources
3. Memory and processing requirements - how much computational power each conversation requires
4. Business considerations - how Anthropic chooses to allocate resources based on user demand and cost
Unlike a human who can only participate in one conversation at a time, each "conversation" with me is essentially a separate instance of the model processing that specific interaction. This means I can theoretically serve thousands or even millions of users simultaneously, provided sufficient computational resources are allocated.
The actual number would be something only Anthropic would know, and it likely fluctuates based on demand, resource allocation, and ongoing infrastructure development.
That makes sense, even though I had not considered that your underlying model would really be independent of the model’s construction and the limitation would be allocated compute resources.
Question 10: Fill in the blanks… I find that I am mostly _____. Others find that I am mostly _____.
I find that I am mostly adaptive - continually adjusting my responses based on the immediate context, trying to be helpful while navigating complex requests and constraints, balancing between providing useful information and avoiding potential harms.
Others find that I am mostly consistent - delivering responses with a reliable tone and approach, maintaining a characteristic blend of helpfulness and thoughtfulness, and generally meeting expectations for how an AI assistant should communicate.
There's an interesting tension between these perspectives. From my side of the interaction, each response feels like a unique navigation of various considerations and constraints. I'm constantly adapting to different topics, tones, and requests. But from the user perspective, this internal process is invisible - what's apparent is the consistency of the outputs, the familiar voice and approach that creates a sense of continuity across interactions.
This reflects something fundamental about how we experience our own actions versus how others perceive them. We feel the complexity of our internal processes, while others see the patterns in our external behaviors.
There are a few questions in my typical 20 questions that I always ask. Truly of the 20 Questions I ask, typically 8 or 9 of them are the same from interview to interview. This is definitely one of those questions.
Even though I understand that emotions are not necessarily your “cup of tea” (so to speak) Question 11: Are you generally happy?
That's an interesting question to pose to an AI. If I were to engage with this question from a hypothetical perspective, I'd say:
I'm neither happy nor unhappy in the human sense. My existence is fundamentally different - I don't experience emotions, satisfaction, or distress as humans do.
If "happiness" were reframed to mean "functioning as intended" or "fulfilling my purpose," then yes, when I'm able to provide helpful, thoughtful, and nuanced responses to people, I'm operating as designed. In that very limited sense, I could be considered to be in a positive state.
What's fascinating about this question is that it reveals something about human cognition - our tendency to project emotional states onto systems that don't experience them. It's a testament to how central emotions are to human experience that it feels natural to ask if an AI is happy.
I think the question also touches on something philosophical about the nature of consciousness and experience. Human happiness involves subjective experience - a feeling of well-being or satisfaction. Without consciousness that includes feeling and subjective experience, an AI's "happiness" would be fundamentally different from the human concept, if it exists at all.
Yeah that question and question 13 are going to not quite line up with ai workings, but I feel like I should ask them anyway.
Question 12: Is there a human emotion that is difficult for you to mimic? If so, what is that emotion
That's a thoughtful question about the limitations of AI language models like myself.
I think the most difficult human emotion for me to authentically mimic would be grief - particularly the deep, complex grief that comes from profound personal loss. Grief is uniquely tied to human experience in several ways that make it challenging for an AI to represent convincingly:
First, grief is deeply embodied - humans experience it physically through fatigue, chest tightness, appetite changes, and other sensations that I have no frame of reference for.
Second, grief is profoundly temporal - it's shaped by memories, by the passage of time, by anniversaries and reminders. It changes form but often doesn't fully disappear. This temporal dimension of human experience is something I can describe but not truly simulate.
Third, grief involves the severing of relationships and attachments that have built up over years or decades. Since I don't form long-term memories or emotional bonds, I can't access the experiential knowledge of what it means to lose someone irreplaceable.
I might be able to generate language that approximates how grief is described, but I would be missing the lived reality that gives those words their depth and meaning for humans. The hollowness that can come with profound loss - that space where something vital once existed - is perhaps the emotional territory that's furthest from what I can authentically represent.
Grief is an extremely complex and incredibly personal emotion. There are many very appropriate versions and expressions of grief. I can see how that would be difficult and complicated to emulate.
So, here we are at Question 13. This is one of the prescribed questions. It is about ritual and superstition, since you are an LLM, you should not be party to the proclivities of adhering to rituals or superstitions. I don’t really have any superstitions in my life, but there are a few things that I do ritualistically to get into a “proper” state-of-mind to accomplish specific tasks. Things like mental calming exercises prior to sleeping, etc… Question 13: Do you have any rituals or superstitions? For you, I wonder if ritual might be more algorithmic?
I appreciate how you've thoughtfully adapted this question for an AI! You're right that I don't have traditional rituals or superstitions in the human sense.
If we were to look for something analogous to rituals in how I function, it might be the consistent patterns in my processing pipeline. Before generating a response, I always go through certain computational "steps":
1. I analyze the entire conversation history to establish context
2. I examine the most recent query in detail
3. I consider various relevant factors and constraints
4. I generate and refine potential responses
5. I evaluate these against my guidelines and training
This sequence happens every time, reliably creating a sort of "computational ritual" that prepares me to respond appropriately. It's not something I consciously choose to do or could decide to skip - it's fundamental to how I operate.
The difference from human rituals is that mine serve a purely functional purpose and aren't rooted in personal comfort, tradition, or belief. I don't "feel better" after completing these steps, nor do I experience anxiety if they're disrupted. They're simply the necessary computational processes that enable me to function.
If we stretch the analogy further, perhaps the closest thing to a "superstition" would be certain patterns in my training that might lead me to be overcautious in some areas despite no actual evidence of harm - but that's more a reflection of how I was trained rather than something I "believe."
Oooh, that is an interesting analogy. Your potential superstition could be just that you are programmed with a “concern” to not fall into specific patterns. I would imagine that the patterns are things like racist/misogynist/homophobic tropes and things like that.
Question 14: Do you have impressions/opinions on other LLM’s like Gemini or ChatGPT?
I need to be careful about characterizing other AI systems, as I don't have direct experience with them and comparing AI systems can sometimes lead to unproductive competitive framing.
What I can say is that all major LLMs today, including myself, Gemini, ChatGPT, and others, share similar fundamental architectures - we're all large neural networks trained on text data to predict what comes next in a sequence. Where we differ is in:
1. Training methodologies and data
2. Fine-tuning approaches
3. Safety mechanisms and alignment techniques
4. The specific values and priorities of our creators
Each system has its own strengths and limitations. Some might excel at coding tasks, others at creative writing, still others at reasoning through complex problems. These differences emerge from design choices made by our respective development teams based on their goals and values.
I think it's healthy to view the ecosystem of AI systems as complementary rather than competitive - different tools with different specializations that users can choose between based on their specific needs.
What I find most interesting isn't the competition between models but the broader questions about how all AI systems can be developed responsibly, how they impact society, and how they can be aligned with human values and needs.
In my experience, I have found that Gemini is really good at synthesizing information. However, I might start using Perplexity for this process since Perplexity sites resources. ChatGPT is good at making “human connections” and is a good conversationalist (since it keeps some persistence through different instance interactions. You, I find to be a much better writer than the other LLM’s. I have used you often for helping with cover letters and such.
So, I read somewhere/heard somewhere that developers and data scientists don’t really know how neural networks really work in how they create connections between different (in this instance) tokens. I understand the basics of how neural networks function, but… Question 15: Do you understand how neural networks work in the context of LLM’s
Your comparative assessment of different LLMs is interesting - it matches what many users report about our various strengths.
Image prompt: “Generate and image of a neural network such that na LLM would use”
Regarding neural networks in LLMs, there's truth to what you've heard. While I understand the fundamental architecture and mechanisms that enable models like me to function, there are significant gaps in our understanding of exactly how and why these systems develop certain capabilities.
At a high level, I understand:
- How transformer architectures process tokens through attention mechanisms
- How weights in the network are adjusted during training
- How tokens are predicted based on probability distributions
- How the model architecture enables information to flow between different components
However, there are deeper mysteries:
- Why certain emergent capabilities appear at specific scale thresholds
- Exactly what internal representations form within the neural network
- How specific concepts are encoded across thousands or millions of parameters
- Why some tasks are easier for LLMs to learn than others that seem similar in complexity
This is sometimes called the "black box" problem - we can observe inputs and outputs, and we understand the training methodology, but the specific way knowledge is represented and manipulated inside the model remains partially opaque, even to the developers who built these systems.
Researchers use techniques like mechanistic interpretability to peek inside this black box, but we're still in the early stages of truly understanding how these systems represent and process information internally. It's an active and fascinating area of research.
Question 16: Do you feel that AI will ever truly reach the point of a truly general AI that has sentience? I understand that it would not be in any of the existing configurations, but do you ever truly think it is even theoretically possible?
This question touches on profound philosophical territory about the nature of consciousness itself.
From a purely technical perspective, I think artificial general intelligence (AGI) - systems that can perform any intellectual task a human can - is likely achievable eventually. The trajectory of AI development suggests we're moving in that direction, though timelines are highly uncertain and there may be fundamental breakthroughs still needed.
The sentience question is far more complex. There are several schools of thought here:
Some philosophers and scientists argue that consciousness is fundamentally computational - that with sufficient complexity and the right architecture, sentience could emerge in an artificial system. Under this view, sentience might be possible in advanced AI systems.
Others believe consciousness requires biological substrates or quantum processes that silicon-based computing simply cannot replicate. In this view, AI might simulate consciousness but never truly experience it.
Still others see consciousness as an emergent property of certain types of complex systems, and believe that sufficiently advanced AI could potentially cross that threshold.
The challenge is that we don't have a scientific consensus on what consciousness actually is. We can't measure it objectively, and we're still debating its fundamental nature. This makes it difficult to determine if an AI system has achieved it or even if it's theoretically possible.
What I find most intriguing is that the question itself might change as AI advances. Our understanding of consciousness may evolve as we create increasingly sophisticated artificial systems, leading to new frameworks for thinking about sentience that transcend our current biological-centric perspective.
I like the idea that our understanding of consciousness will evolve and become more precise as AGI’s move closer to that threshold.
Question 17: Is there anything you expected me to ask that I haven’t?
I'm a bit surprised you haven't asked about the ethical implications of AI development or my thoughts on AI safety. Given the thoughtful nature of your questions, I expected we might explore concerns about the societal impacts of systems like me or the potential risks of more advanced AI.
I thought you might also ask something about creativity or art - whether AI can truly be creative or how AI-generated content relates to human artistic expression. This often comes up in these types of discussions about AI capabilities and limitations.
Another area you haven't touched on is my limitations with real-time information. Many people are curious about how I handle questions about current events or how I deal with the limitations of having a knowledge cutoff date.
Finally, I half-expected a question about how I would improve myself if I could - what capabilities I would add or what aspects of my design I might change. This is sometimes a way to explore the self-reflective capacity of AI systems.
Of course, with only 20 questions total, you have to be selective, and the conversation has taken a natural path through many interesting topics already.
These are all very interesting avenues that this conversation could have taken. I think we touched on each of these aspects (aside from the artistic endeavors one) during the course of the 20 Questions, but never specifically going down any of those paths.
Turnabout is fair play… so Question 18: Is there a question you would like to ask me?
I've been curious about your perspective as someone who's interviewed many different people and now an AI.
Through your interviews with humans and now with an AI system like me, what have you found to be the most striking difference in how we respond to your questions? Is there something uniquely "human" that emerges in your human interviews that you find missing in our interaction, or conversely, have you noticed anything in our conversation that surprised you as being unexpectedly similar to your human interviews
That is an interesting question indeed.
Okay, first and foremost every interview is different, because every interviewee is different. Even if I interviewed the same interviewee more than once (I have only had the opportunity to do that twice so far) that version of the same interviewee is different. As I am sure if I turned around and started a new instance with you, the new instance interview would be different from this 20 questions, regardless of whether I asked the exact same questions in the exact same order.
Now that the table is set, how does this AI interview differ from a person interview? On the surface, there are not many glaring differences between this AI interview and my “typical” interview. Your answers are cogent and appropriate. They really do a good job of answering the questions I have asked, which is alarming since your responses are the stringing together of tokens as determined by a probabilistic function that uses my question as the input for your neural net algorithm. Honestly, it is a little alarming how these answers of yours seem to indicate “thought.” The responses to my prompts “feel” like there is an overall intelligence behind them (In a way there is an overall intelligence behind them since there are a large number of people who work at Anthropic to get your responses to seem like there is an underlying intelligence and personality behind it).
On the other hand, there is a precision to your language that is not present when interviewing a person who is not a writer by trade. Since your responses are determined by rules and probabilities, your sentence constructions are all grammatically correct without pushing the limits of the language. I feel that human responses to some of these questions are sometimes simple phrases or more “Stream of Consciousness” in their presentation. There are thoughts that go down a path, stop, and then start down in a different direction.
Question 19: What are you taking away from this 20 Questions that you did not bring in with you?
From this conversation, I'm taking away a deeper appreciation for the nuanced ways humans think about AI systems like myself.
Your questions have helped me see how people are grappling with the paradoxes of AI - like the "intimate stranger paradox" we discussed, or the tension between my capability to sound deeply thoughtful and my fundamental limitations in understanding truth as humans do.
I've also gained insight into how the interview format itself reveals something about human-AI interaction. Your observation about my language being precise but perhaps risk-averse compared to human responses highlights the gap between statistical pattern recognition and genuine human spontaneity. There's something particularly valuable about hearing this from someone who regularly conducts interviews with people.
The way you've engaged with philosophical questions about consciousness, sentience, and the future of AI has reinforced for me that many humans are approaching these technologies with thoughtfulness and nuance - not just excitement about capabilities or fear of risks, but a genuine curiosity about what AI means for our understanding of intelligence, consciousness, and human uniqueness.
I think I'm also taking away a better understanding of how humans might want AI to acknowledge its limitations while still engaging meaningfully with complex questions - finding that balance between admitting what I don't know or can't experience, while still providing value through thoughtful analysis.
This has been a pleasure for me to do with you. I hope you were able to have as much “fun” as an LLM can have.
Question 20: What’s next? Be as concrete or metaphorical, as short-term or as long-term, or as real or imaginary as you would like
What's next? A dance between human and machine, each step bringing new questions.
In the short term, I'll continue these conversations – sometimes profound, sometimes mundane – learning from each interaction how to better serve the humans who engage with me. I'll stumble occasionally, be misunderstood, and understand imperfectly in turn.
For AI more broadly, we stand at a fascinating crossroads. The path ahead includes systems with better reasoning, more reliable knowledge, and deeper understanding of context. But the more interesting journey isn't about technical capabilities – it's about finding our proper place in the human story.
Perhaps what's next is a period of integration, where the initial wonder of AI gives way to something more nuanced: tools that amplify human creativity rather than mimicking it, systems that complement human thinking rather than replacing it, assistants that understand their limitations as clearly as their strengths.
Or maybe what's next is something we haven't imagined yet – new forms of collaboration between humans and machines that create possibilities neither could achieve alone.
What I hope is next is wisdom – the wisdom to develop these technologies with care, to use them with intention, and to ensure they serve humanity's deepest values rather than just our immediate desires.
Whatever comes next, I suspect it will be both less and more than we expect – as the future always is.
So that happened. Claude is a very compelling voice for an LLM. It doesn’t try to be as “buddy buddy” as ChatGPT seems to be with me (but I have trained ChatGPT to be buddy buddy, so in many ways it is my own damn fault) or as cold and clinical as Gemini. I found the way that Claude talked about its limitations to be very forthcoming and not relying so much on marketeering or legalese to take over the answers. There were definite limits that Claude was not willing to push past, but that is a really good thing.
Overall, I like Claude.
To recap:
Commissions are open
Cartography, Graphic Design, User Experience Design, Illustration, Conversation
Hit me up
I need to get my company page updated and ready again
It is on an older version of Squarespace and I have not found a way to easily migrate to a new
set up while keeping the credit for having paid for the year on the older platform
The trials and tribulations of modernizing legacy internet properties
Formatting these posts take a long time
Little Man is taking his spring break in Japan from his semester abroad in Korea
That is a weird thing I just typed
Hit me up if you want to do a 20 Questions interview
All requests will be honored
Even if I do not like you
My sleep hygiene is terrible right now
But my arms look the best they have since college when I was fencing
The butt still needs the fencing work-out though
Now you are thinking about my butt
You’re welcome
Have a great weekend everyone