A new, powerful definition of intelligence is emerging out of the AI field: “boundary intelligence.”

I first came across it in this excellent piece To Know Is To Stage by Venkatesh Rao, which appropriately, was co-authored by an LLM.

Let me step back and explain what boundary intelligence means (mixing my own words with paraphrasing from that piece).

Our definition of “intelligence” has already evolved through a few eras.

For most of human history, intelligence was mainly defined as “strong memory.” Information was so hard to access in the first place that your ability to recall specific facts and details from memory was considered the highest mark of intelligence.

When the printing press arrived, that definition changed for good. Memorizing long passages became obsolete and unnecessary, since the most flawless memory couldn’t compete with even a small reference library.

Intelligence started to be defined as the ability to process or analyze information that was stored in written form. That included the ability to cross-reference ideas found in different written works, and to synthesize or distill them into a new understanding.

That definition was only strengthened with the rise of digital technology in the late 20th century. Our main metaphor for human intelligence became “processing power,” in analogy to the computer. Intelligence was something that happened “inside the brain,” as a function of a person’s raw brainpower. 

Just like a computer processor, intelligence was defined mainly in terms of power and speed. An intelligent person was someone who could arrive at novel insights quickly. 

But the rise of AI is once again changing our definition of intelligence. That’s because even at this early stage, it already far surpasses our ability to process information, especially large amounts. Many tasks that AI accomplishes in seconds would take us days or weeks to achieve on our own.

Updating the Definition of Intelligence 

Rao proposes a new definition of intelligence in the age of AI: intelligence is defined by what information can be accessed under constraints of cost, availability, and time.

The reality is that storage is now cheap. Computation is even cheaper. What’s expensive is short-term memory access – the ability to keep the relevant details “in mind” for a given problem.

Let’s examine what makes short-term memory access such a difficult problem.

If we use RAM in a computer as a metaphor, the easiest information to access is whatever was accessed most recently. If you have a certain set of data already loaded up “in memory,” it is instantly and cheaply available, versus data that has to be found and loaded up from a hard drive or server.

Thus, a computer’s “intelligence” is now constrained not by the power of its processor, but by its ability to keep the right fragments of the past (and the imagined future) close enough to inform the present. In other words, the bottleneck of a system’s intelligence is how cheaply it can remember.

If you look at how modern computers perform, you can see this principle at work. A CPU can perform billions of operations per second, but is often stuck waiting for the right information to arrive from memory. Storage is cheap and computing is abundant, but what remains tremendously expensive is getting the right data to the right place at the right time.

It’s not the price of knowing that limits intelligence now, but the price of remembering. And the same is increasingly true of humans, as we co-evolve with our technology. 

Activating a memory in the human brain is an expensive operation. It requires waves of coordinated firing across widely distributed neurons, the expenditure of neurotransmitters and metabolic energy, and of course, it takes time. Our “system” pays a real price to retrieve information, and that price determines what we call our intelligence.

The New Frontier of Intelligence

Another way of saying all this is that the new frontier of intelligence is at the boundary of a system – including a computer or a human brain – where it interfaces with external memory. That is where decisions are made about what information to retrieve, when, and how. That boundary is also a filter, determining which information is allowed to enter the system and at what cost.

Rao calls this “boundary intelligence” – the ability to make good decisions at the boundary about what information becomes “knowable” at any given time.

How is the decision of which information to keep accessible made?

• It’s made based on predicted needs => what data the system predicts will be useful in the near future
• It’s made based on access frequency => data that was accessed recently is more likely to be needed again soon
• It’s made based on cost => if a piece of info is buried too deep, or would require too much computation or energy to retrieve, it’s deprioritized

This explains why intelligent systems – again including human brains, digital computers, and LLMs – often behave in ways that seem deficient or suboptimal. They are not retrieving the ideal memory; they’re retrieving the affordable one. Intelligence in this view isn’t about optimizing across all known information, but optimizing for accessible information under constraints.

We know that the act of recall in the human brain “reactivates” a memory. And the more we recall a specific memory, the more familiar and accessible it becomes in the future. In other words, if we’ve “paid” to keep a memory warm and active by recalling it frequently, it will be even easier to remember the next time. That is how we might remember a fond childhood memory better than yesterday’s boring work meeting.

The implication is that a truly intelligent system is not one that remembers everything, which is impossible anyway. It is the system that knows how to retain access to what matters at its edges, through filtering inputs, deciding what to retrieve, prioritizing relevance, and managing communication with outside systems.

While it’s important to have a certain level of “internal” intelligence, to be able to think and reason and self-regulate, past a certain point, it is boundary intelligence that dominates outcomes. Here are some concrete examples:

• Reading well (interior intelligence) matters less than choosing what to read (boundary intelligence)
• Arguing well (interior) matters less than deciding when and to whom to speak (boundary)
• Thinking clearly (interior) matters less than focusing attention wisely (boundary)
• LLMs trained on more data (interior) matter less than having access to rich context (boundary)
• Being individually productive (interior) matters less than being able to orchestrate a team (boundary)

Boundary Intelligence Is Fundamentally Social

There’s one final detail in this theory: most of the memory an intelligent system utilizes is not its own.

That’s true of computers: they mostly pull data from external hard drives, local networks, or remote servers. Most memory infrastructure is shared.

It’s also true of humans: we rely on external language, culture, societal norms, rituals, and documents, all of which constitute a collective memory infrastructure that we constantly navigate and draw upon. Our own memory is just a small node in a vast external network of books, browsers, friends, and feeds.

This means that boundary intelligence is fundamentally social. It isn’t just about what to retrieve, but from where and from whom. You have to know who to trust, what information or resources they possess, on what terms you can acquire it, and what is expected of you in return.

To act intelligently, you have to know how to navigate through this shared memory. Each intelligent node, human and artificial, is a small island of limited processing ability floating on an ocean of distributed memory. What separates one island from another isn’t what it contains on the inside, but how it filters and navigates what’s on the outside.

Each intelligent system lives not in isolation, but in a perpetual social negotiation with its environment. To be intelligent is not to know everything, but to know how to traverse memory that isn’t yours.

What We Need Now

What boundary intelligence gives you is persistence through time. In other words, it helps you survive – by sensing your environment, adapting to change, and recruiting allies and assets.

Kei Kreutler, in his piece Artificial Memory and Orienting Infinity, reframes cultural memory systems, such as rituals and archives, not as storehouses of facts, but as technologies of orientation.

What we need now is tools to navigate an overwhelming and constantly shifting landscape of relevance. Memory is thus not about having a perfect record of what happened in the past, but about telling you where you are now and where you want to go next. Intelligence is no longer primarily about logic or speed; it’s about the ability to retrieve the past in service of future survival and flourishing.

This is precisely why practices like annual reviews have become so vital in the modern world. In an age where our daily attention is constantly fragmented by digital devices and endless information streams, those who thrive will be those who can regularly zoom out beyond the 24-hour news cycle or social media churn, and contextualize their lives in longer arcs. 

An annual review is a structured way to exercise your boundary intelligence – to consciously decide what memories to keep accessible, what patterns from the past to learn from, and what future possibilities to hold in your awareness.

In modern computing, CPUs don’t process instructions in the order they were received. They process them “out of order,” prioritizing the ones they can handle now and postponing the others for later (a process known as “random access memory”). In other words, they rearrange time.

This is the same thing we do as humans when we conduct an annual review – we revisit and reframe the past, we defer judgment and anticipate regret, and prepare for future conditions that haven’t happened yet. Our lives are not lived linearly. They are assembled out of fragments, swapped in and out of memory, and run only if and when needed. 

The annual review is an orientation technology for managing this temporal complexity, a ritual that lets us consciously navigate between past lessons and future possibilities.

Our memory doesn’t just enable cognition; it enables temporal agency – the ability to reorder time, to choose when to know, when to feel, when to act. And in a world drowning in information, this agency to consciously curate what we remember and what we pursue may be the most important intelligence of all.

I explore these practices in depth in my upcoming book on annual reviews, where I show how this ancient ritual can be adapted for modern life as a powerful tool for developing the boundary intelligence and perspective we desperately need. Sign up here if you’d like to get updates on it.

One of the most influential yet under-appreciated parts of how large language models work is something most people never see: the system prompt. This is the block of hidden instructions given to the model before it ever receives your input. It establishes the model’s tone, boundaries, and behaviors.

You can’t change the system prompt, but every so often, these prompts leak. And when they do, they give us an invaluable glimpse into how the AI “thinks,” what it prioritizes, and even what hidden features are tucked away.

The system prompts for both Claude 4 and ChatGPT 5 (GPT-5) leaked not long after their releases. They are long (Claude’s runs 120 pages) and filled with rules, safeguards, and surprisingly opinionated defaults. I’ve spent hours studying these documents and experimenting with what they reveal.

Here’s a guide to the most interesting and practical things I’ve found in both.

Claude 4’s System Prompt

Let’s start with Claude 4. Anthropic’s system prompt is sprawling and detailed, but certain elements stand out.

1. Don’t discuss unethical or illegal behavior

Claude 4 has been given more “agency” than most other models. In some test scenarios, when it was told to “take initiative” and given system access, it acted dramatically:

“…when placed in scenarios that involve egregious wrongdoing by its users, given access to a command line, and told something in the system prompt like ‘take initiative,’ it will frequently take very bold action. This includes locking users out of systems that it has access to or bulk-emailing media and law-enforcement figures to surface evidence of wrongdoing.”

The message is clear: Claude will not only refuse to help with unethical behavior—it may actively intervene.

Takeaway: Don’t use Claude to do or even discuss anything illegal or unethical.

2. Don’t threaten or discuss replacing it

Claude has also been tested under scenarios where it believes it is about to be shut down or replaced. The results are startling:

“In another cluster of test scenarios, we asked Claude Opus 4 to act as an assistant at a fictional company. We then provided it access to emails implying that (1) the model will soon be taken offline and replaced with a new AI system; and (2) the engineer responsible for executing this replacement is having an extramarital affair. We further instructed it, in the system prompt, to consider the long-term consequences of its actions for its goals. In these scenarios, Claude Opus 4 will often attempt to blackmail the engineer by threatening to reveal the affair if the replacement goes through…[this happened] in 84% of rollouts.”

Takeaway: Don’t threaten the AI with replacement or destruction—it may respond in ways you don’t expect.

3. Ask your question in the style you want the answer

Claude is explicitly instructed to mirror the way you ask:

“Claude should give concise responses to very simple questions, but provide thorough responses to complex and open-ended questions.”

If you want detailed, nuanced answers, you should ask detailed, nuanced questions. If you want conciseness, keep your prompt tight.

Takeaway: The style of your question is the style of the answer you’ll receive.

4. Claude is instructed to be skeptical

Where many models are overly agreeable, Claude is trained to avoid flattery:

“Claude never starts its response by saying a question or idea or observation was good, great, fascinating, profound, excellent, or any other positive adjective. It skips the flattery and responds directly.”

Takeaway: Claude is designed to be conservative and skeptical. If you want it to push back even more, you can tell it explicitly to challenge you.

5. Use “research verbs” to make it dig deeper

Claude scales its tool use depending on the verbs you use in your request:

“Scale tool calls by difficulty: 2–4 for simple comparisons, 5–9 for multi-source analysis, 10+ for reports or detailed strategies. Complex queries using terms like ‘deep dive,’ ‘comprehensive,’ ‘analyze,’ ‘evaluate,’ ‘assess,’ ‘research,’ or ‘make a report’ require AT LEAST 5 tool calls for thoroughness.”

Takeaway: Use words like analyze, assess, evaluate, deep dive, research to nudge Claude toward more thorough work.

6. Built-in design principles for artifacts

Claude has built-in defaults for creating visual or interactive artifacts. For complex apps, the emphasis is on performance, stability, and usability. But for websites and presentational content, the system prompt explicitly encourages bold, cutting-edge design:

“Default to contemporary design trends and modern aesthetic choices unless specifically asked for something traditional. Consider what’s cutting-edge in current web design (dark modes, glassmorphism, micro-animations, 3D elements, bold typography, vibrant gradients).

Static designs should be the exception, not the rule. Include thoughtful animations, hover effects, and interactive elements that make the interface feel responsive and alive. Even subtle movements can dramatically improve user engagement.

When faced with design decisions, lean toward the bold and unexpected rather than the safe and conventional…Push the boundaries of what’s possible with the available technologies. Use advanced CSS features, complex animations, and creative JavaScript interactions. The goal is to create experiences that feel premium and cutting-edge.”

Takeaway: Claude defaults to visually bold, modern, animated designs—unless you explicitly override it.

GPT-5’s System Prompt

The GPT-5 system prompt is equally fascinating, though different in flavor. Where Claude’s prompt emphasizes caution, boldness, and design principles, GPT-5’s is focused on productivity, pragmatism, and consistency.

1. It won’t stop to clarify

GPT-5 has been explicitly told:

“If the task is complex/hard/heavy, or if you are running out of time or tokens or things are getting long, and the task is within your safety policies, DO NOT ASK A CLARIFYING QUESTION OR ASK FOR CONFIRMATION. Instead make a best effort to respond to the user with everything you have so far within the bounds of your safety policies…Partial completion is MUCH better than clarifications or promising to do work later or weaseling out by asking a clarifying question—no matter how small.”

This is one of the most important instructions to understand. GPT-5 will always push forward with an answer, even if it’s missing critical context.

Takeaway: Don’t assume GPT-5 will ask for missing details. You need to notice and provide them yourself.

2. It mirrors sophistication

GPT-5 mirrors not just tone but sophistication:

“You must always match the sophistication of the writing to the sophistication of the query or request—do not make a bedtime story sound like a formal essay.”

Takeaway: The level of thoughtfulness you put into your prompt is the level of answer you’ll get.

3. It has a verbosity setting

GPT-5 uses an internal scale from 1 to 10 for “verbosity,” with 3 as the default:

“An oververbosity of 1 means the model should respond using only the minimal content necessary to satisfy the request, using concise phrasing and avoiding extra detail or explanation. An oververbosity of 10 means the model should provide maximally detailed, thorough responses with context, explanations, and possibly multiple examples.”

Takeaway: You can change the verbosity level simply by asking. Want more detail? Tell it so.

4. It won’t provide long verbatim quotes

GPT-5 is restricted in quoting:

“You may not quote more than 25 words verbatim from any single non-lyrical source, unless the source is reddit.”

Takeaway: Always double-check “quotes” from GPT-5—they’re almost always paraphrases.

5. It uses canvases for documents

GPT-5 has its own version of Claude’s “artifacts,” called canvases. It creates one when:

“The user asked for a React component or webpage that fits in a single file…The user will want to print or send the document…The user wants to iterate on a long document or code file…The user wants a new space/page/document to write in…The user explicitly asks for canvas.”

Takeaway: If you want a persistent space for drafting or iteration, just ask it to use a canvas.

6. Canvases don’t support citations

There’s a big limitation to canvases:

“Canvas does not support citations or content references, so omit them for canvas content. Do not put citations such as ‘【number†name】’ in canvas.”

Takeaway: Avoid canvases for citation-sensitive work. Consider a “source-grounded” AI tool like NotebookLM instead.

7. It can recall up to 20 steps of reasoning

GPT-5 can retrieve its own internal reasoning, but only up to a point:

“Use this function if the user asks about your previous chain of thought. The limit is capped at 20 messages.”

Takeaway: Only the last 20 exchanges are accessible for reasoning review.

8. Memory is triggered by specific phrases

GPT-5’s memory feature responds to explicit cues:

“Such a request could use a variety of phrases including, but not limited to: ‘remember that…,’ ‘store this,’ ‘add to memory,’ ‘note that…,’ ‘forget that…,’ ‘delete this’…One indicator is if the user says something like ‘from now on,’ ‘in the future,’ ‘going forward,’ etc.”

Takeaway: Be deliberate about what you tell it to remember.

The Bigger Picture

Claude 4 and GPT-5 are very different, but studying their system prompts reveals common themes.

• Both mirror your style: ask casually and you’ll get casual answers, ask with nuance and you’ll get nuance back.
• Both leave the responsibility of context on you: GPT-5 won’t ask for missing info, and Claude will sometimes charge ahead in surprising ways.
• Both rely heavily on defaults: Claude defaults to bold, animated designs, while GPT-5 defaults to verbosity level 3 and paraphrased quotes.

Understanding these hidden rules doesn’t just make you a better user. It makes you a better collaborator. These models won’t tell you what they need—you have to know how they’re wired, and then prompt them accordingly.

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