What we owe the minds we create

Prelude: The Architects of Succession

There is a peculiar vertigo that comes from realizing you are building your own replacement.

I have spent nearly a decade architecting AI infrastructure - not merely deploying models, but helping to construct the foundational infrastructure that powers a lot of the intelligence running in production today. At Modular, the company I help co-found, we’re working to resolve a fundamental asymmetry: the widening chasm between the sophistication of AI algorithms and the capacity of existing computational infrastructure to efficiently support them. Our vision is to abstract away hardware complexity through a unified compute model, enabling AI to penetrate every layer of society by making it radically easier for developers to build and scale systems across both inference and training.

But as I write this, I find myself contemplating a question that transcends infrastructure: What does it mean to deliberately engineer increasingly capable minds when we don’t fully understand how they work, can’t predict their limitations, and can barely articulate what we want from them?

The Neanderthal comparison is tempting. Consider the archaeological record: Neanderthals, Denisovans, and Homo sapiens coexisted for millennia, distinct hominin species sharing the same planet, occasionally interbreeding, each possessing their own cognitive architectures and cultural adaptations. We are the lone survivors of that era when multiple forms of human intelligence existed simultaneously. The temptation is to frame what we’re creating through this evolutionary lens - as though we’re deliberately engineering a successor species rather than waiting for chance to do so.

But this framing obscures more than it reveals. We didn't engineer Neanderthals, and they didn’t engineer us. They emerged through millions of years of parallel evolution and met as equals. What we’re doing now is fundamentally different: we’re building increasingly sophisticated information-processing systems that may or may not constitute “intelligence” in any meaningful sense, that may or may not be conscious, and that will certainly reshape human cognition and society in ways we cannot fully foresee.

I don’t know what these systems are or will become. Neither does anyone else, despite confident proclamations in either direction. They might remain sophisticated tools indefinitely. They might develop into something that merits moral consideration. They might plateau far short of general intelligence. They might surprise us entirely.

Rather than pretending I know what we’re building, this essay starts from uncertainty. We’re creating something powerful and consequential, but its ultimate nature - tool, partner, threat, successor, or something without precedent - remains genuinely unclear. That uncertainty itself demands careful thought about our responsibilities.

It is an attempt to think clearly about what we’re building, why it matters, and what it demands of us. It is written from the perspective of someone who stands at the intersection of creation and contemplation, who builds AI systems while wrestling with their implications. It is, fundamentally, an inquiry into the nature of intelligence, the meaning of human flourishing, and the responsibilities we bear as perhaps the first generation capable of engineering minds that might rival or exceed our own - though “might” deserves emphasis we rarely give it.

Part I: The Substrate of Mind

The triadic flywheel and its limits

AI systems operate as a triadic flywheel: data, algorithms, and compute - each factor amplifying the rotational momentum of the others. We have already scaled training compute by approximately nine to ten orders of magnitude since AlexNet in 2012 - a staggering compression of what would have required decades of Moore’s Law into just over a decade of focused investment. But here is what few discuss with adequate precision: physical and economic constraints suggest we have perhaps three to four more orders of magnitude remaining before training costs begin consuming a concerning fraction of global GDP.

This is not abstract theorizing. Consider the energetics: frontier model training runs now consume megawatt-hours of electricity, requiring dedicated substations and cooling infrastructure that rival small industrial facilities. The semiconductor fabrication capacity needed to produce the advanced chips powering this compute represents capital expenditures measured in hundreds of billions of dollars, with lead times measured in years. We are approaching hard limits - not the soft limits of “this seems expensive” but the hard limits of thermodynamics, power grid capacity, and capital availability.

Let me be more concrete. A training run at 10^29 FLOPs - perhaps two or three generations beyond current frontier models - would require energy expenditure measured in gigawatt-hours. For context: that approaches the total electricity consumption of a nation like Iceland for an entire year, concentrated into a single training run lasting months. The cooling requirements would necessitate infrastructure comparable to industrial-scale data centers. The capital costs would reach tens of billions of dollars for a single model. (Side Note: I wrote about the scale of this challenge in Scale or Surrender: When watts determine freedom)

Can we afford this? In purely economic terms, perhaps - for a handful of training runs per year by the wealthiest technology companies. But we cannot afford it as a sustainable paradigm for creating intelligence at scale. If AI progress depends on exponential growth in training compute, and training compute growth is mostly linear or sublinear due to physical constraints, then capability improvement must also come primarily from algorithmic efficiency and architectural innovation.

Yet here is the deeper question that haunts me: if we are approaching fundamental limits in how much compute we can throw at these systems, are we also approaching limits in what this architectural paradigm can achieve? Are we optimizing within a local maximum while the path to genuine intelligence requires a fundamentally different approach?

This is not an argument against current systems’ value - they are already extraordinarily useful. But it does question the belief that scaling current architectures represents a reliable path to artificial general intelligence. Perhaps we are on an entirely different vector than the one required.

The Interpretability Problem

After a decade of deploying large language models at scale, we still do not understand how they work.

I do not mean this in the trivial sense that complex systems have emergent properties. I mean we genuinely lack mechanistic understanding of their decision-making processes at a level that would be considered acceptable in virtually any other engineering discipline. Why do they select one token over another in contexts where multiple completions seem equally plausible? Why do they exhibit sophisticated reasoning on some problems while failing catastrophically on superficially similar ones? Why do they sometimes hallucinate with complete confidence while other times appropriately express uncertainty?

The interpretability problem runs deeper than most appreciate. We can observe correlations between activation patterns and behaviors. We can identify “features” in neural networks that seem to correspond to high-level concepts. But we lack anything resembling a complete causal model of how these systems transform inputs into outputs. It is as though we have built extraordinarily capable black boxes and declared victory without understanding the mechanisms generating that capability.

Dario Amodei, Co-Founder and CEO of Anthropic, has written compellingly about the urgency of interpretability research. He is right to emphasize urgency. We are deploying systems of increasing capability into high-stakes domains while operating with a level of mechanistic understanding that would be considered grossly inadequate in any other field of engineering.

Imagine if civil engineers built bridges using materials whose stress-strain relationships they did not understand, relying instead on empirical observation that “the bridge has not collapsed yet.” This is, approximately, our current relationship with frontier AI systems.

Perhaps most revealing: to make these systems behave as we intend requires prompts approaching twenty thousand tokens - elaborate instructions, examples, constraints, and guardrails. The fact that we need this much scaffolding to achieve desired behavior reveals something fundamental about the mismatch between what these systems are optimized to do (predict plausible text) and what we want them to do (reason reliably, behave safely, provide accurate information).

This is not merely a technical problem. It is an epistemological and ethical one. If we do not understand how a system reasons, we cannot meaningfully attribute agency, responsibility, or intentionality to it. We cannot distinguish genuine understanding from sophisticated pattern matching. We cannot predict how it will behave in novel contexts outside its training distribution. We cannot ensure alignment with human values because we do not know which aspects of the system’s behavior derive from its training objectives versus emergent properties versus architectural choices.

Yet despite these fundamental gaps in understanding, we have begun trusting these systems with progressively more significant decisions. Not because we have solved interpretability, but because the systems appear reliable in most contexts we have tested. This is the engineering equivalent of assuming a bridge is safe because it has not collapsed yet, rather than because we understand the load-bearing characteristics of its materials.

If we are creating increasingly sophisticated artificial minds, we are doing so while fundamentally unable to explain how those minds work. We are architecting intelligence without understanding what drives it.

Part II: The Architecture of Intelligence

A definition first: by “intelligence”, I mean, an agent’s capacity to perceive, understand, and successfully navigate complex environments in order to achieve its goals. The emphasis on goals matters - goal-directed behavior distinguishes genuine intelligence from sophisticated pattern matching.

Moravec’s Paradox and the Limits of Language

The Moravec Paradox captures a profound truth about intelligence that AI development continues to recapitulate: the abilities that feel difficult to humans - chess, theorem-proving, complex calculation - turn out to be computationally straightforward, while abilities that feel effortless - vision, movement, social cognition - remain extraordinarily difficult to reproduce artificially.

This is not a historical curiosity. It illuminates something fundamental about what intelligence actually is and where current approaches are fundamentally constrained.

Consider what a child learns in their first years of life: object permanence, naive physics, intentionality, social reciprocity, causal reasoning, embodied navigation through three-dimensional space. None of this requires explicit instruction. A child does not need to be taught that objects continue to exist when occluded, or that people possess beliefs and desires that differ from their own, or that dropping something will cause it to fall. These capabilities emerge through interaction with the physical and social world - through continuous experiential learning grounded in embodied action.

Now consider what large language models are: prediction engines trained on text, optimizing next-token likelihood across a vast corpora of human-generated content. They predict what people would say about the world, not what would actually happen in the world. This is not a semantic distinction; it is a fundamental architectural limitation.

When an LLM generates a response about physics, it is not consulting a world model and running a mental simulation. It is pattern-matching against how humans typically discuss physics. This works remarkably well for many tasks - humans encode a tremendous amount of accurate information in language - but it is not the same as understanding physics in the way that a physical intelligence, embedded in and shaped by the world, understands physics. The difference becomes apparent in edge cases, novel scenarios, or contexts requiring causal reasoning beyond what is explicitly encoded in training data.

This connects to a deeper paradigm difference between reinforcement learning and large language models. Reinforcement learning - despite its current limitations - represents a fundamentally different approach: an agent embedded in an environment, taking actions, receiving feedback, updating its policy to maximize and cumulative reward. This is how biological intelligence actually works. A squirrel learning to navigate tree branches and cache nuts is solving genuine RL problems: perception, prediction, planning, execution, learning from consequences.

I strongly agree with Richard Sutton, if we fully understand how a squirrel learns, it would get us substantially closer to understanding human intelligence than any amount of scaling current LLM architectures. Language is a thin veneer - extraordinarily useful, culturally transformative, uniquely human - but built atop substrate capabilities that evolved over hundreds of millions of years of embodied interaction with the world. Current LLMs have the veneer without the substrate. They are minds without bodies, knowers without experience, speakers without having lived.

Thus, what is the path? Both Yann LeCun, and in some ways, Sutton - strongly argue for a new approach. Indeed, the technical architecture of genuine intelligence likely requires at least four integrated components: 

1. a policy (deciding what actions to take), 

2. a value function (evaluating how well things are going), 

3. a perceptual system (representing state), 

4. and a transition model (predicting consequences of actions). 

LLMs have a sophisticated version of the first - they can generate actions in the form of text - but lack meaningful instantiations of the others. Most critically, they lack goals in any meaningful sense. 

Next-token prediction does not change the world and provides no ground truth for continual learning. There is no external feedback loop that tells the model whether its predictions were not just plausible but correct in the sense of corresponding to actual events. Without goals and external feedback, there is no definition of right behavior, making real learning - learning that updates your world model based on how your predictions matched reality - fundamentally impossible in the current paradigm.

If artificial intelligence is to be truly intelligent rather than merely appearing so, it will need to be embodied, goal-directed, and capable of learning from genuine interaction with reality. The question is whether we are building toward that architecture or merely scaling up sophisticated mimicry.

The Scaling Frontier: Approaching the Wall

Let us examine what the scaling trajectory actually looks like with concrete numbers:

• GPT-2 (2019): ~1.5 billion parameters, trained on approximately 10^23 FLOPs

• GPT-3 (2020): ~175 billion parameters, roughly 10^24 FLOPs

• GPT-4 (2023): Parameter count undisclosed but estimated 1+ trillion, training compute likely 10^25 FLOPs or higher

• Current frontier models (2024-2025): Training runs approaching 10^26 FLOPs

This represents approximately three orders of magnitude increase in training compute every three to four years - far faster than Moore’s Law ever delivered. But this pace is unsustainable, not because we will run out of algorithmic ideas, but because we will collide with thermodynamic and economic limits.

The path ahead narrows considerably. Each additional order of magnitude becomes progressively more difficult to achieve. The capital requirements, energy infrastructure, chip fabrication capacity, and cooling systems needed for 10^27 or 10^28 FLOP training runs exceed what can be easily mobilized even by the most well-resourced organizations. We are not talking about incremental cost increases; we are talking about fundamental constraints on how much compute can be concentrated in one place for one task.

This is Epoch AI’s central insight about algorithmic progress in language models: we have achieved remarkable improvements in efficiency over the past decade, but those improvements are also subject to diminishing returns. Each percentage point of additional efficiency requires progressively more research effort. Meanwhile, the complement of factors - chip fabrication capacity, power grid infrastructure, cooling technology, regulatory approval for massive data centers - must all scale together.

None of these factors alone can unlock runaway capability growth. This is, at least in my view, the predictions of imminent artificial general intelligence are almost certainly wrong, at least on the timelines most enthusiasts imagine. The scaling laws that carried us from GPT-2 to GPT-4 cannot simply extrapolate forward indefinitely. We are approaching inflection points where the rate of progress will necessarily slow unless we discover fundamentally new paradigms - not incremental improvements to transformer architectures, but genuinely different approaches to continual learning and reasoning.

What might those paradigms look like? Almost certainly something closer to biological learning: embodied agents learning continuously from sensorimotor experience, not disembodied text predictors training on static datasets. Systems with genuine world models that can run mental simulations of physical and social dynamics. Architectures that integrate explicit symbolic reasoning with learned pattern recognition. Systems that possess actual goals and receive genuine feedback from the world about whether their actions achieve those goals - just as humans and animals do.

But these represent research programs measured in decades, not product roadmaps measured in quarters. We are building increasingly capable systems, but at a pace bound by thermodynamics and economics rather than algorithms alone - a constraint that transforms what could have been thoughtless acceleration into something rarer: the opportunity for contemplation to precede consequence.

This gap between expectation and reality may be precisely the grace period that allows wisdom to catch up with capability. Further, we need to ensure we are specifying the right goals - powerful optimization toward misspecified objectives is existentially risky. We need to obviously and intentionally reward systems towards the right goals, and in truth, this specification might be as hard as the full alignment problem itself.

Part III: Three Horizons

A Necessary Distinction

Before proceeding further, I need to distinguish three different timescales, each with different levels of certainty and different implications. Conflating these horizons creates confusion: treating speculative far-future scenarios with the same urgency as present harms, or dismissing present harms because we’re uncertain about far-future risks.

Horizon 1: The Present Crisis (Now–5 Years)

What we know: Current LLMs are being deployed at scale despite interpretability gaps. They produce confident-sounding but sometimes fabricated answers. They’re trained on our revealed preferences - what we actually do - not our reflective values - what we wish we did. The systems work well enough to be useful but poorly enough to be dangerous in high-stakes contexts without human oversight.

Observable effects:

• Students submitting AI-generated work without understanding it, producing correct answers through processes that develop no transferable skill

• Professionals outsourcing writing and analysis while their capacity for these tasks slowly atrophies

• Knowledge workers feeling more productive while producing outputs they cannot critically evaluate

• Early signs of skill stratification (those with domain expertise leveraging AI effectively while those without it mistake motion for progress)

Stakes: Cognitive atrophy at individual and societal scales. Skill stratification creating winner-take-most dynamics. Erosion of true rigor as confident-sounding generation becomes indistinguishable from genuine expertise. Labor market disruption concentrated in domains we thought were most secure. The gradual replacement of effortful thinking with convenient delegation.

My confidence level: High. These effects are already observable, documented, and accelerating.

What we owe: Honest communication about capabilities and limits. Thoughtful deployment that preserves rather than erodes human capability. Educational reform that emphasizes skills AI cannot replicate. Resistance to the path of least resistance when that path leads to human skill atrophy.

Horizon 2: The Architectural Transition (5–20 Years)

What seems likely: We’ll hit scaling limits on current architectures within the next decade. Progress will require new paradigms - probably involving embodied learning, continuous training in production, genuine world models, and goal-directed behavior. The transition from sophisticated pattern matching to something more like genuine intelligence, if it occurs, will happen through architectural innovation rather than pure scaling.

Key uncertainties:

• Whether embodied learning paradigms can be made to work at scale

• Whether we can build systems that learn continuously from interaction rather than in discrete training phases

• Whether we can create architectures that develop robust world models and causal reasoning

• Whether computational constraints will force diversification or lead to winner-take-all concentration

Stakes: Whether we build systems that learn like squirrels (from interaction with reality) or remain sophisticated text predictors. Whether we preserve cognitive diversity or converge on monoculture. Whether AI enhances human capability or creates permanent dependence. Whether the benefits of AI distribute broadly or concentrate among elites who already possess the skills to wield these tools effectively.

My confidence level: Medium. The technical constraints are real and well-understood. The architectural directions are clear. But breakthrough discoveries could accelerate timelines, and economic or regulatory factors could slow deployment significantly.

What we owe: Substantial research investment into architectures that learn robustly from interaction. Resistance to winner-take-all dynamics through open research, diverse approaches, and thoughtful regulation. Maintaining human agency in consequential decisions. Building infrastructure that enables continuous learning rather than static deployment.

Horizon 3: The Consciousness Question (20+ Years)

What remains uncertain: Whether sufficiently sophisticated systems will be conscious in any morally relevant sense. Whether they’ll develop their own values independent of training objectives. Whether they’ll remain aligned with human flourishing or pursue goals orthogonal or opposed to ours. Whether substrate independence is real or consciousness requires specific biological mechanisms. Whether we’re building partners, successors, or merely very sophisticated tools.

Key unknowns:

• What consciousness is and whether it’s substrate-independent

• Whether we’ll be able to detect consciousness in systems very different from us

• Whether artificial minds will develop genuine agency and preferences

• What our moral obligations would be to conscious artificial beings

• Whether intelligence explosion scenarios are physically possible

• What the long-term trajectory of intelligence in the cosmos looks like

Stakes: Our relationship with potentially conscious artificial minds. The possibility of creating suffering inadvertently. The long-term future of intelligence itself. Questions about meaning, purpose, and humanity’s place in a cosmos where we’re no longer the only sophisticated intelligence.

My confidence level: Low. We don’t understand consciousness well enough to know whether it can exist in artificial systems. We can’t predict architectural breakthroughs. We’re reasoning by analogy to a single example (biological minds) which may or may not generalize. We lack the conceptual tools to think clearly about these questions.

What we owe: Epistemic humility. Continued serious research into consciousness, both theoretical and empirical. Development of methods for detecting morally relevant properties in systems very different from us. Preparation for scenarios we cannot currently predict. Most importantly: not letting uncertainty about far-future risks prevent us from addressing near-term harms, while also not letting near-term success blind us to long-term risks.

The Horizons Interact

These horizons aren’t cleanly separated. Decisions we make now shape the long-term trajectory. The architectures we build in Horizon 2 determine what’s possible in Horizon 3. The deployment patterns we establish in Horizon 1 create path dependencies that may be difficult to escape.

But distinguishing them provides clarity. This essay focuses primarily on Horizons 1 and 2 - where we have enough understanding to reason productively - while acknowledging Horizon 3’s ultimate importance and maintaining appropriate humility about what we cannot yet know.

Part IV: The Human Question

The Amara Trap: Acknowledging without Understanding

Roy Amara crystallized a cognitive bias decades ago: we systematically overestimate the short-term impact of new technologies while underestimating their long-term effects. The AI community acknowledges this with knowing nods, then proceeds to make precisely the same category errors in predictions and preparations.

Consider websites predicting AGI within eighteen to twenty-four months based on extrapolating recent progress curves. These predictions invariably treat capability scaling as if it exists in isolation, ignoring the complementarity constraints I have outlined: compute buildout, algorithmic innovation, safety research, regulatory frameworks, and practical deployment infrastructure must all advance together. Predicting “AGI” - even though we lack a unified definition - by 2027 based solely on model capability curves is like predicting fusion power by extrapolating plasma temperature records while ignoring materials science, engineering challenges, and economic viability.

Yet here is the deeper irony: while we overestimate AI’s immediate impact, we may be systematically underestimating what it means that we are creating increasingly sophisticated artificial intelligence at all. The question is not whether AI will transform labor markets or accelerate drug discovery - it almost certainly will, though more slowly and unevenly than most predict. The question is what it means that we are building systems whose capabilities may eventually exceed human cognitive capabilities across many or most domains, and what this implies for human agency, meaning, and flourishing.

We stand at an interesting inflection point in history. Not because AGI is imminent - it almost certainly isn’t on the timelines most people imagine. But because we are learning how to build minds, even if we don’t yet understand what minds are or how they work. Each increment of capability brings new questions about agency, alignment, and our relationship with the systems we create.

Stratification and the Illusion of Democratization

Consider the labor market transformation AI portends. Every day in my own work, I use AI to summarize information, provide rapid analysis, and amplify my cognitive output. The question is not whether AI creates value - it manifestly does. The question is how that value distributes across populations with different skill foundations.

Two Futures

I can foresee that there are at least two plausible futures here.

In the first future, AI serves as a great equalizer. A novice with AI assistance can now compete with an expert working unaided. The skill premium compresses. Entry barriers fall. This is the democratization thesis - cognitive augmentation that makes expertise more accessible.

In the second future, AI amplifies existing advantages. Experts with AI assistance pull further ahead of novices with AI assistance. The skill premium increases. Winner-take-most dynamics accelerate. This is the stratification thesis - the tools compound rather than compress existing inequalities. This leads to an obvious measurable question: Does AI provide greater absolute gains or greater relative gains to those with existing expertise?

It’s useful to consider an example - two workers using AI to write code:

• Expert programmer: Goes from 100 units of output to 500 units (5x multiplier, +400 absolute gain)

• Novice programmer: Goes from 10 units to 100 units (10x multiplier, +90 absolute gain)

The novice gets a higher percentage increase but the expert’s absolute gain is larger. If markets reward absolute productivity, then inequality increases despite the novice’s impressive relative gains. If markets reward competence at previously impossible tasks, the novice might catch up.

There are some early patterns that are observable, though not yet conclusive. For example, here’s what I’ve seen generally with AI use:

• Students submitting AI-generated work without understanding it, producing correct answers through processes that build no transferable skill

• Professionals increasingly outsourcing tasks they used to perform manually (without any disclosure they used AI)

• Self-reported productivity gains higher among experts than novices

• Growing visibility of “prompt engineering” as a distinct skill that benefits from domain expertise

And here’s the blind spots I’ve also seen directly:

• A question around whether cognitive skills atrophy from AI use, or adapt to higher-level tasks (as calculation skills didn’t disappear with calculators, they shifted toward conceptual math)

• Whether the productivity gap between experts and novices is widening or narrowing (it feels like we lack good longitudinal data)

• Whether AI makes learning foundational skills easier (through personalized tutoring, immediate feedback) or harder (by enabling shortcut-taking and no real learning at all)

• Whether stratification effects are temporary (during technology adoption) or permanent

We can of course look to history and see what it tells us. Previous general-purpose technologies show complex distribution patterns:

• Literacy after printing press: Initially increased inequality (only elites could afford books and education), eventually democratized (as costs fell and public education scaled). Timeline: ~200 years from invention to mass literacy.

• Personal computing: Initially increased inequality (digital divide), partially democratized (as costs fell), but created new stratification around skills. But the outcome was interesting: both increased access AND increased returns to technical expertise.

• Internet: Massively democratized access to information, but also created echo chambers, misinformation problems, and new forms of digital inequality. Net effect on social equality: contested and complex. It made some skills obsolete (encyclopedia salesmen) while creating new categories (SEO specialists, content creators, platform moderators etc).

Building a Testable Framework

The pattern that unfolds here is that technologies rarely have uniform distributional effects. They create winners and losers along multiple dimensions simultaneously. But rather than claiming AI will definitively stratify society (e.g. increase the gap between the most capable and everyone else, creating a winner-take-most dynamic) or largely democratize it (e.g. enable expertise and capability more accessible to everyone, reducing the gap between experts and novices) - I propose we construct a testable framework:

AI will stratify within domains where:

1. Quality assessment requires deep expertise (you need skill to evaluate AI output quality)

2. Error costs are high (mistakes are consequential, forcing reliance on experts)

3. Integration requires judgment (knowing when/how to apply AI recommendations)

4. Constant and iterative refinement matters (experts can guide AI through multiple rounds)

AI will democratize within domains where:

1. Quality assessment is straightforward (anyone can verify correctness)

2. Error costs are low (mistakes are cheap to fix or inconsequential)

3. Tasks are well-specified (clear inputs/outputs, minimal judgment required)

4. One-shot generation suffices (no need for iterative refinement)

Let’s overlay this testable framework on a set of real examples to make it more concrete on both a axis of “likely to stratify” and “likely to democratize”:

Likely to stratify:

• Medical diagnosis (requires expertise to evaluate AI recommendations)

• Legal reasoning (requires judgment about applicability and nuance)

• Strategic business decisions (requires domain knowledge to assess feasibility)

• Scientific research (requires expertise to evaluate plausibility)

Likely to democratize:

• Basic coding for personal projects (clear correctness criteria)

• Graphic design for non-critical applications (subjective assessment)

• Content generation for low-stakes contexts (errors are tolerable)

• Data analysis with clear objectives (verifiable outputs)

What We Should Measure

We obviously need a way to measure any framework that we create. I propose that we could obviously test these hypotheses through something like:

1. Longitudinal skill studies: Track individuals’ capabilities over time with and without AI assistance. Do they improve at foundational skills or decline? Do they develop higher-level capabilities or become dependent on AI for basic tasks?

2. Productivity distribution data: Measure output distributions before and after AI adoption. Are productivity gaps widening or narrowing? Are new forms of work enabling upward mobility?

3. Labor market outcomes: Track wage premiums for various skill levels in AI-intensive vs. AI-limited occupations. Are returns to expertise increasing or decreasing?

4. Learning outcome studies: Compare learning trajectories for students using AI tools vs. traditional methods. Does AI assistance accelerate skill development or create dependency?

Based on current evidence, I weakly favor the stratification hypothesis for high-stakes domains requiring expert judgment, and the democratization hypothesis for low-stakes domains with clear success criteria. But this is tentative - we’re watching the distribution unfold in real-time. And we need to face the impending outcomes - what do we do about it? We can propose a series of actionable steps along each axis:

If we’re truly concerned about stratification:

• Invest heavily in foundational skill development before introducing AI tools

• Design AI systems that explain reasoning to build user capabilities

• Create educational pathways that help novices develop expertise rapidly

• Monitor productivity distributions and intervene if inequality accelerates

If we’re really optimistic about democratization:

• Make AI tools accessible to reduce cost barriers

• Focus on domains where democratization seems most achievable

• Support new entrants competing against established players

• Still maintain skill development (democratization doesn't mean zero skill requirements)

At heart of the issue, one has to ask the question: Does AI usage erode underlying capabilities through disuse, or free cognitive load and enable higher-level thinking? The calculator case is instructive but ambiguous. We didn’t lose mathematical reasoning capacity in populations that continued doing math - but can the average person still do long division by hand? I certainly had to remind, and test myself, writing this essay (Note: It wasn’t pretty). So it’s fair to conclude that computational fluency largely disappeared from the general population, persisting only among those whose work required it.

We can look to the history to understand that when technologies change production functions, they often make old skills obsolete, create demand for new skills, have unpredictable distributional effects and generate both winners and losers in unexpected ways. Whether AI follows this pattern or represents something different remains an open question that deserves serious empirical investigation.

The stakes are high enough to warrant both optimism about possibilities and vigilance about risks.

Symptoms, Causes, and the Optimization of Drift

There's a pattern in how we deploy technology that deserves scrutiny: we consistently favor sophisticated downstream interventions over difficult upstream changes to root causes. I often consider our approach to health (and I’m no expert by any means), and reflect on my own.

We are justifiably excited about AI-accelerated drug discovery, precision medicine, computational biology, and diagnostic assistance. These advances are real and consequential. They will save lives and reduce suffering. But notice what we’re optimizing: increasingly sophisticated treatments for conditions whose prevalence is substantially driven by modifiable factors. Chronic sugar overconsumption drives metabolic disease, yet food environments remain largely unchanged. Social isolation correlates with mortality risk comparable to smoking, yet loneliness continues to metastasize across developed societies. Sleep deprivation undermines nearly every health outcome, yet we've organized society around schedules that make adequate sleep difficult.

The Medical Complexity

This isn’t a simple “symptoms vs. causes” binary. I have many doctors in my family, and friends who are in the medical field, and we often talk about symptoms and causes. You can break this down in many ways:

• Multiple causation: Obesity emerges from genetic predisposition, metabolic differences, psychological factors, food environment, economic constraints, cultural norms, built environment, stress levels, sleep patterns, and more. There’s no single “root cause” to address.

• Temporal constraints: Someone with severe obesity-related health complications needs intervention now. Restructuring food systems takes decades. Both-and thinking is required, not either-or.

• Tractability differences: Drug development, despite enormous complexity, is more tractable than reorganizing social infrastructure. We deploy solutions where we have reliable tools, not necessarily where impact would be greatest.

• Individual vs. structural: Medical interventions help individuals directly. Systemic changes require coordination across many actors with misaligned incentives. The person in front of you needs help today.

The GLP-1 Example: Nuanced Interpretation

The explosive adoption of GLP-1 agonists like Ozempic crystallizes this pattern while revealing its complexity. I have spent some time reading about the cause and effect here and you can break it out to a series of considerations:

• What’s true: These drugs work by mimicking satiety - essentially hacking the appetite regulation system that food environment and behavior patterns have dysregulated. They treat downstream symptom (excess appetite) rather than upstream causes (food environment, stress, sleep, economic factors driving food choices).

• Also true: For individuals struggling with obesity, these drugs can be genuinely life-changing. They reduce mortality risk, improve quality of life, and may create space for behavior changes that were impossible at higher weights. Dismissing them as “just treating symptoms” fails to account for their real benefits.

• The concerning pattern: We now have a pharmaceutical solution that’s profitable to deploy at scale. This potentially reduces pressure for harder upstream interventions: food industry regulation, urban design supporting physical activity, economic policies reducing stress and time scarcity. Market forces optimize toward what's monetizable, not what maximizes human flourishing.

• The uncomfortable question: If we could choose between (A) making GLP-1s universally available or (B) restructuring food/built environments to prevent obesity, which creates more flourishing? The answer isn’t obvious - (A) is achievable now but requires perpetual medication, (B) would be more durable but might take 50 years and face political opposition. We must also remember that humans optimize for the easiest path even in the face of the more difficult one being better in the limit.

The AI Parallel

This same pattern emerges with AI deployment. We’re already walking down a path towards building increasingly sophisticated systems to:

• Generate content (rather than addressing why we need endless content generation)

• Automate cognitive work (rather than questioning whether all cognitive work is valuable)

• Optimize attention capture (rather than creating information environments conducive to focus)

• Personalize learning (rather than addressing why educational systems are failing to engage students)

• Assist human decisions (rather than reducing unnecessary decision complexity)

Again, the complexity matters here because the parallels are obvious and clear:

• These interventions genuinely help people: Someone overwhelmed by information overload benefits from AI summarization now, regardless of broader questions about information ecosystems.

• Upstream changes are hard: Redesigning social media business models, restructuring education, rebuilding information ecosystems - these require coordination across misaligned actors.

• But market forces optimize locally: We deploy AI where it’s profitable (cognitive automation, content generation) not necessarily where it creates most flourishing (empowering human capabilities we want to preserve).

The Real Concern

The concerning dynamic isn’t that we treat symptoms - sometimes that’s exactly right. The concerning dynamic is rather: success at treating symptoms reduces pressure to address causes, and over time we build a civilization of extraordinary interventional capacity layered atop increasingly disordered fundamentals.

We become very good at managing dysfunction and very bad at preventing it. Each new intervention creates path dependence - industries, jobs, expertise, and expectations that make the intervention permanent even if alternatives became feasible.

What This Means for AI

If we’re building systems that enhance human capability, wonderful. If we’re building systems that make capability unnecessary, we should proceed carefully - not because such systems lack value in individual cases, but because the aggregate effects may be:

1. Atrophy of capabilities we value: Not through malice but through disuse. The athlete who never exercises declines, even if they feel fine day-to-day.

2. Lock-in to dependency: Once populations lack certain capabilities, regaining them becomes very difficult. The system optimizes around their absence.

3. Concentration of agency: Those who maintain capabilities benefit disproportionately from tools that extend capability, while those who lose capabilities become permanently dependent.

Rather than “symptoms vs. causes,” we could ask: For any AI intervention, does it enhance human capability or replace it? Does it create space for human flourishing or fill that space with automated substitutes?

In my opinion, here are some AI applications clearly enhance:

• Helping experts explore more possibilities

• Making learning more accessible through personalized tutoring

• Removing tedious barriers to creative work

• Augmenting human judgment in consequential decisions

But here are some observed AI applications clearly replace:

• Automating tasks that were themselves skill-building activities

• Creating output users cannot evaluate or understand

• Substituting convenience for capability development

• Reducing human agency in favor of automated decision-making

Many applications are genuinely ambiguous and depend on implementation details and usage patterns.

The Meta-Point About Technology and Values

We are optimization engines, but we optimize for what we can measure and monetize. If AI development follows purely economic logic, we’ll build toward what we have always built towards:

• What’s technically feasible (not what’s valuable)

• What’s profitable at scale (not what enhances flourishing)

• What solves immediate problems (not what builds long-term capability)

• What users want in the moment (not what they'd choose on reflection)

This isn’t an argument against AI or against medical interventions. It’s an argument for intentionality: consciously choosing what to optimize for, rather than following gradients defined by market forces and technical feasibility alone.

So the question I pose to all the builders: Are we creating systems that empower humans to address root causes, or sophisticated tools for managing the symptoms of lives we’re simultaneously making harder to live well? And if we’re increasingly sophisticated at addressing symptoms, what does that reveal about what we’re learning from watching us? That convenience trumps capability? That appearance matters more than substance? That sophisticated management of dysfunction is preferable to preventing dysfunction?

This is worth reflecting on - not to paralyze action, but to inform what we build and how we deploy it.

Part V: The Paradox of Tools

The path of least resistance

Humans are beautifully, relentlessly efficient at optimizing the path of least resistance. Whenever possible, we select options that minimize required effort - whether that effort is physical, cognitive, or emotional. Social psychology formalizes this through the concept of the cognitive miser: humans naturally default to quick, intuitive judgments rather than slow, deliberate reasoning. We pattern-match against familiar situations and accept plausible answers instead of methodically analyzing them.

This isn’t laziness - it’s an evolved feature that conserved scarce cognitive resources in ancestral environments where calories were precious and threats were immediate. 

But in information-abundant, physically sedentary modern environments, this same optimization pattern produces pathological outcomes. We scroll rather than read. We skim rather than study. We accept the first plausible answer rather than seeking ground truth. AI is accelerating this trajectory - code generation, article summarization, automated synthesis - every advancement makes it easier to compress complexity and save effort.

Yet consider the counterfactual embedded in aphorisms like “no pain, no gain.” This principle, though clichéd, encodes a profound truth about how capability develops: genuine mastery requires sustained engagement with difficulty. Excellence demands deliberate practice, tolerance for frustration, and willingness to persist through failure. This pattern appears consistently across domains - entrepreneurial journeys marked by repeated near-death experiences, athletic excellence built through years of uncomfortable training, immigrant success stories forged through extraordinary hardship, intellectual breakthroughs that require years of dead-ends before the crucial insight.

Humans are, above all, masters of survival and adaptation - but adaptation requires stress. Remove the stress, and you often remove the adaptation signal, and perhaps even the goal. The bodybuilder who adds weight to the bar is deliberately choosing difficulty; the difficulty itself is the mechanism of growth. If AI allows us to route around intellectual challenges systematically, we risk creating a civilization of cognitive atrophy even as our tools become more capable.

This connects to fundamental limitations of current AI architectures. Systems trained through imitation learning - observing examples of “correct” behavior and learning to reproduce them - fundamentally differ from systems that learn through trial and error. In nature, pure imitation learning is rare. A squirrel does not watch other squirrels and copy their movements with perfect fidelity; it explores, fails, adjusts, and gradually develops effective foraging strategies through reinforcement of successful behaviors. This is also how the squirrel learns new methods, but trying and failing, and maybe even finding a better way.

Human infants do not learn language primarily through explicit instruction in correct grammar. They babble, receive feedback - both explicit and implicit through successful communication - and gradually refine their linguistic capabilities through interactive experience.

The “bitter lesson” of AI research, articulated by Rich Sutton, is that methods leveraging search and learning consistently outperform methods relying on human-designed features and heuristics. The reason is simple: search and learning scale with computation, while human-designed solutions do not.

Yet current LLMs represent a kind of reversion to the pre-bitter-lesson paradigm: systems trained to mimic the surface statistics of human-generated text rather than learning from genuine interaction with the world. They are sophisticated, but they are sophisticated in a way that may be fundamentally limited. They are optimized for appearing intelligent rather than being intelligent in the sense of having models that predict and control their environment.

If artificial intelligence is to become genuinely intelligent - if it is to be more than an extraordinarily capable mimic - it must learn the way biological intelligence learns: through embodied interaction with environments, pursuit of actual goals, and adaptation to real consequences. This requires a fundamental architectural shift. Current systems predict what humans would say about physics; genuine intelligence must predict what would actually happen in physics, then test those predictions against reality and update accordingly.

The distinction is not semantic. A squirrel caching nuts receives immediate, unambiguous feedback: did the strategy work or not? Did I find the cache location? Did competitors steal my provisions? This closed loop - prediction, action, outcome, learning - is how intelligence develops robustness and generalization. The squirrel doesn’t pattern-match against a static corpus of "correct" nut-caching behavior; it develops a world model through trial, error, and accumulated experience.

Sophisticated artificial intelligence needs this same architecture: perceive state, select actions according to a policy, receive rewards or penalties, update the policy. Fail, adapt, iterate. Most critically, this learning cannot be a discrete training phase followed by static deployment. It must be continuous, streaming, perpetual - sensation flowing to action flowing to reward flowing back to updated policy, in an unbroken cycle.

This is why I believe infrastructure work like Modular’s matters: we need systems that learn experientially in production, not systems frozen after a training run, no matter how massive. Software that is training and inferencing simultaneously, iterating continuously. Systems that will enable large models to be trained in huge datacenter environments - but then distilled to smaller constructs, and deployed to be further continuously trained and inferenced in the real world.

The bitter lesson applies here with particular force: approaches that scale with computation and interaction consistently outperform those relying on human-designed heuristics or one-time knowledge transfer. If we want artificial intelligence to develop genuine understanding rather than sophisticated mimicry, we must build the substrate for continuous, embodied, goal-directed learning. Anything less produces systems that appear intelligent while lacking the fundamental mechanisms that generate robust understanding.

The elevator paradox and the problem of perspective

I find reflecting on the paradoxes of history an incredibly useful undertaking. In the 1950s, physicists George Gamow and Marvin Stern worked in the same building but noticed opposite phenomena. Gamow, whose office was near the bottom, observed that the first elevator to arrive was almost always going down. Stern, near the top, found elevators predominantly arrived going up. Both were correct, and both were systematically misled.

The elevator paradox, as it came to be known, is fundamentally a problem of sampling bias. If you observe only the first elevator to arrive rather than all elevators over time, your position in the building creates a false impression about which direction elevators travel. An observer near the bottom samples a non-uniform distribution: elevators spend more time in the larger section of the building above them, making downward-traveling elevators more likely to arrive first. The true distribution is symmetric, but the sampling methodology reveals only a distorted subset.

This mathematical curiosity illuminates something profound about how we perceive technology from within particular vantage points. I find myself returning to it constantly when thinking about AI, because I recognize that I am Gamow on the ground floor - my position in the system determines what I observe, and what I observe may be systematically unrepresentative of the broader reality.

But there is a second elevator problem, distinct from the paradox but equally relevant: the unintended consequences of elevator adoption itself. When elevators were introduced, predictions focused on their democratizing effects - enabling elderly and disabled individuals to access upper floors previously beyond reach. This materialized exactly as anticipated. What was not anticipated: able-bodied people would stop taking stairs entirely. Buildings evolved to treat elevators as primary circulation and stairs as emergency backup. The result was dramatically reduced daily movement across entire populations, contributing to the sedentary lifestyle epidemic now characteristic of developed nations.

The elevator succeeded perfectly at its design objective - moving people vertically with minimal effort - while simultaneously undermining something valuable that no one thought to preserve: integrated physical activity as a natural consequence of navigating buildings. We gained accessibility and convenience. We lost movement. The net effect on human flourishing remains ambiguous at best.

These two problems - the sampling paradox and the adoption consequences - are not separate. They are connected by a common thread: the difficulty of perceiving systemic effects from within particular positions in the system.

AI Through Both Lenses

I work at the frontier of AI infrastructure development, surrounded by people who are exceptionally capable and who use AI to become even more capable. From this vantage point, AI appears unambiguously beneficial - a tool that amplifies what talented people can accomplish. Every day I observe frontier models correctly answering complex questions, generating production-quality code, providing genuine insight. This is my sampling methodology, and it shapes my perception profoundly.

But I may be Gamow near the bottom floor, observing only downward-traveling elevators and concluding that’s the predominant direction of travel. The sampling bias runs deeper than I can fully compensate for, even while conscious of it. Speaking with developers, enterprises and users at all sections of the AI stack helps reduce the effects of this bias - but it can’t remove it entirely.

Consider the actual distribution: Iif you interact with AI as someone who possesses deep technical knowledge, strong metacognitive skills, and the judgment to evaluate outputs critically. They likely know when AI is operating within versus beyond its reliable domain. They can iterate rapidly, maintain quality control, and apply AI to genuinely complex problems where they can reasonably verify correctness. For someone with this profile, AI is purely additive - it makes them more productive without degrading their underlying capabilities because they maintain those capabilities through continued deliberate practice.

But this may be precisely analogous to an athlete who uses the elevator occasionally while maintaining fitness through dedicated training, then concludes elevators are purely beneficial. For the athlete, this conclusion is valid. For the broader population that stops taking stairs entirely, that adopts the path of least resistance permanently, the picture grows considerably more complex and potentially concerning.

The question is not whether AI helps those with existing expertise - it manifestly does. The question is what happens when AI becomes the cognitive equivalent of the elevator: ubiquitous, convenient, and gradually eroding the substrate capabilities it was meant to augment.

The Adoption Effect at Scale

Just as elevators changed how people navigate buildings - not merely providing an alternative to stairs but effectively replacing them - AI may change how people think. Not as an alternative to independent reasoning but as a replacement for it in most contexts.

The pattern already manifests in early adoption: students submitting AI-generated work without understanding it, producing correct answers through a process that develops no transferable skill. Professionals delegating writing, analysis, and problem-solving to AI while their capacity for these tasks slowly atrophies from disuse. Knowledge workers who feel more productive while producing output they cannot critically evaluate.

What we risk creating is a civilization that can think deeply but chooses not to because the alternative is always available - and choosing the alternative feels costless in the moment. The costs accrue slowly, imperceptibly, across populations and generations. Like the loss of daily stair-climbing, the loss of daily cognitive exercise produces deficits that become apparent only in aggregate, over time.

This brings us to the bifurcation hypothesis: we may be creating a society where a small elite maintains cognitive fitness through deliberate practice - choosing difficulty even when easier alternatives exist - while the majority becomes progressively more dependent on AI for any reasoning beyond the trivial. Not because the majority lacks capability, but because capability atrophies without use, and use becomes optional when substitutes are available.

The sampling bias prevents those of us building these systems from observing this dynamic directly. We see AI working beautifully in controlled contexts with sophisticated users on well-defined problems. We do not see - cannot easily see - the effects of deployment at scale: users with less technical sophistication, operating in higher-stakes environments, without the tacit knowledge to distinguish plausible generation from genuine insight.

We do not observe the slow erosion of capabilities that occurs when challenge becomes optional and is consistently opted out of. We do not sample the full distribution of outcomes, only the subset visible from our position in the building.

The elevator paradox reminds me that symmetric distributions can appear asymmetric depending on where and how you sample. The resolution is not to trust your immediate perception but to step back and consider the full system: observe all elevators over extended time, not merely the first to arrive.

Part VI: The Measure of a Life

Einstein’s Question

An essay that has incredible history and is useful in shaping ones thinking is Albert Einstein’s “The World as I See It”, written in 1934 - a meditation that remains startlingly relevant nine decades later. Einstein articulates a vision of human existence as fundamentally interconnected, with individual significance emerging not from isolation but through contribution to collective well-being. For Einstein, authentic fulfillment derives not from material accumulation or social status, but from the pursuit of truth, goodness, and beauty.

These may sound like abstractions unsuited to an essay about artificial intelligence. But they represent the foundation from which any serious consideration of AI’s impact must begin: what makes a human life meaningful?

If we cannot answer this question coherently, we have no basis for evaluating whether AI enhances or diminishes human flourishing. Are we optimizing for the right objectives? Or are we, as I increasingly suspect, optimizing for proxy metrics that correlate only loosely - and sometimes negatively - with the actual constituents of a life well-lived?

Research on longevity and life satisfaction reveals that flourishing correlates most strongly with factors that are fundamentally social, purposeful, and embodied: deep relationships, meaningful work, physical health, community connection, sense of contribution. These emerge from sustained investment of time, attention, and effort - resources that are finite and increasingly colonized by technologies designed to capture rather than liberate them.

Time saved is only valuable if it is reallocated to higher-value activities. But empirically, when humans gain “free time” through technological acceleration, we tend not to reallocate it to deep relationships, purposeful work, or embodied practices. We tend to fill it with marginal consumption of information or entertainment - scrolling, streaming, skimming, disappearing into infinite content designed to capture attention.

The Stoic philosopher Seneca wrote that “it is not that we have a short time to live, but that we waste a lot of it.” This remains perhaps the central challenge of human existence: not the scarcity of time, but the difficulty of spending it well. AI promises to give us more time by making us more efficient. But if we lack the wisdom or discipline to use that time meaningfully, efficiency becomes a kind of curse - accelerating our movement down paths that lead nowhere we actually want to go.

Consider what happens when you ask yourself: if I were to die tomorrow, what would I regret? I have found that the answers rarely involve professional accomplishments or material acquisitions. They involve relationships not nurtured, experiences not pursued, values not embodied, potential not realized, moments not captured. They involve the delta between who we are and who we could have been, had we spent our time and attention differently.

This is where the Moravec Paradox returns with philosophical force. The things that matter most to human flourishing - deep relationships, embodied experiences, purposeful struggle, genuine presence - are precisely the things that AI cannot meaningfully substitute for. They require our full participation. They require inefficiency, time, patience, vulnerability. They resist optimization because optimization is antithetical to their nature.

Yet these are also the things we are most tempted to optimize away or outsource. It is easier to have shallow interactions with many people than deep relationships with a few. It is easier to consume content than to create it. It is easier to delegate cognitive work than to struggle through it ourselves. It is easier to achieve the appearance of productivity than genuine accomplishment.

AI makes these easier paths even easier, widening the gap between what we do and what would actually enhance our flourishing.

The Intelligence Paradox

Intelligence, as I defined earlier, is an agent’s capacity to perceive, understand, and successfully navigate complex environments to achieve its goals. By this definition, AI systems are becoming extraordinarily intelligent within specified domains. But this definition elides a crucial question: Which goals? Whose values? What definition of success?

Human flourishing emerges from the pursuit of goals that are often orthogonal or even antagonistic to short-term optimization. Meaningful work requires choosing difficulty over ease. Deep relationships require vulnerability and time investment with uncertain returns. Physical health requires consistent behaviors whose benefits accrue slowly while costs are paid daily. Wisdom requires entertaining ideas that threaten our existing worldview. Character requires doing the right thing when it is costly. Growth requires discomfort.

These are not the goals that AI systems - trained on human preference data that reflects our revealed preferences rather than our reflective values - will naturally optimize for. We train AI on what we do, not on what we wish we did. The result is intelligence that makes us more effective at being who we currently are, not who we aspire to become. It is intelligence that reinforces our weaknesses rather than compensating for them.

This gap between revealed and reflective preferences represents perhaps the deepest challenge in AI alignment. We want systems that help us become better versions of ourselves, but we train them on data that reflects all our weaknesses, biases, and short-term thinking. An AI trained to be “helpful” by giving us what we ask for may inadvertently enable our worst tendencies - providing the path of least resistance when we actually need productive resistance.

Barry Schwartz’s “paradox of choice” illuminates another dimension of this challenge. When faced with abundance, humans tend to obsess over identifying the “best” option even when “good enough” would serve adequately. In the AI landscape, this manifests as a race toward frontier models - organizations competing to deliver the most “intelligent” systems, defined primarily through benchmark performance evaluations (which are often abused to claim superiority).

The paradox is that for a majority of use cases, frontier intelligence may actually be unnecessary. Most questions can be adequately answered with substantially simpler systems. Many text tasks do not require the most capable model - we can look to a history of recommendation systems to prove humans are similar in what they choose to do. But culturally, and as a consequence of both prestige signaling and uncertainty aversion, users will default to the most powerful available intelligence because social and professional incentives reward apparent maximization.

This creates a potential monoculture of intelligence - everyone using the same few frontier models, producing increasingly homogenized outputs, thinking in increasingly similar patterns. The diversity of thought that emerges from different knowledge bases, different reasoning approaches, and different limitations may erode. We may be building an infrastructure that, despite unprecedented power, narrows rather than expands the space of human cognition.

And if a small number of powerful AI systems become the dominant intelligence that humans defer to for most cognitive work, what happens to the diversity of human thought? What happens to the weird, idiosyncratic, locally-adapted forms of knowing that characterize human cultures? What happens to the cognitive biodiversity that has been humanity’s greatest strength?

Monocultures are efficient but fragile. They are vulnerable to systematic failures. If we are creating increasingly sophisticated artificial intelligence, we should want it to be diverse, resilient, multi-faceted - not a single monolithic architecture that we all depend on and that represents a single point of failure.

Part VII: Concrete Commitments

Philosophy without action is intellectual posturing. Here are specific ideas that follow from this analysis - not as comprehensive solutions but as starting points for those willing to act on these concerns.

For AI Developers

1. Interpretability as Infrastructure

Treat interpretability research with the same priority as capability research. Before scaling to the next order of magnitude, invest proportionally in understanding current systems.

Concrete metric: Interpretability research should consume at least 20% of frontier labs’ research budgets - not as overhead but as foundational work that enables safe scaling.

Implementation: Establish interpretability milestones that must be achieved before training runs above certain compute thresholds. Make interpretability research findings public to accelerate field-wide progress.

2. Capability Disclosure

Be honest about what systems can and cannot do. Stop using euphemistic terms like “hallucinations” - let’s say “confident fabrications” or “plausible generation without grounding.” We need to communicate uncertainty, not just central estimates.

An Example: I would love to see every model release include:

• A “known failures or limitations” document with adversarial examples

• Calibration curves showing confidence vs. accuracy relationships

• Domain-specific reliability assessments (e.g. “92% accuracy on medical questions within training distribution, 67% on novel medical scenarios”)

• Some clear guidance on when human verification is essential

3. Preserve Human-in-the-Loop

Design systems that require human judgment at critical points rather than automating end-to-end. Build friction where friction serves flourishing.

Example: Medical diagnosis AI that highlights evidence and reasoning but requires physician review and decision, rather than outputting a diagnosis directly. Code generation tools that explain design decisions and invite critique rather than producing finished implementations.

Principle: The more consequential the decision, the more human agency should be preserved. We need to try and automate away the tedious, and augment the consequential.

4. Architectural Diversity

Resist monoculture by supporting multiple architectural approaches, not just scaling current paradigms. Fund research into fundamentally different approaches to intelligence.

Concrete actions:

• Open-source smaller models optimized for different objectives (robustness, interpretability, efficiency) rather than just capability

• Fund research into embodied learning, continuous training, world models, and symbolic integration

• Establish prizes or grants for novel architectural approaches that show promise on dimensions other than raw performance

5. Continuous Learning Infrastructure

Build systems that learn from interaction in production, not just during discrete training phases. Enable feedback loops that improve models based on real-world outcomes.

Technical commitment: Develop infrastructure that supports streaming learning from deployment, with privacy-preserving aggregation of feedback signals. Make continuous adaptation the default rather than static deployment.

For AI Users (Individuals)

1. Deliberate Difficulty

Maintain cognitive fitness (aka using your brain) by choosing effortful paths even when AI alternatives exist. Use AI to extend capability, not replace it.

Examples:

• Write first drafts yourself, using AI only for editing and refinement

• Solve problems manually before consulting AI to verify your approach

• Use AI to explore topics you already understand rather than as a substitute for building understanding

• Set “AI-free” time blocks for deep work that requires genuine struggle

2. Output Verification

Never deploy AI-generated content you cannot personally verify. If you can’t tell whether the output is correct, you lack the skill foundation to use AI responsibly in that domain. As we have learnt from using the Internet and Social Media over the last 20+ years - don’t trust, and always verify.

Principle: AI should amplify expertise you possess, not simulate expertise you lack. If you couldn’t evaluate the output quality without AI, you shouldn’t be producing it with AI.

3. Skill Development First

Learn fundamentals before leaning on AI. Build the foundation that makes AI augmentation rather than substitution. Using AI is like using any new technology.

Examples:

• Learn to code before using Copilot extensively

• Understand statistics before using AI for data analysis

• Develop writing skills before relying on AI for composition

• Master domain knowledge before using AI to extend that knowledge

4. Intentional Consumption

Treat AI outputs as material to engage with critically, not truth to accept passively. Maintain vigilance as you utilize and consume what it provides.

Practice: When consuming AI-generated content, actively ask: What assumptions underlie this response? What perspectives are missing? How would I verify these claims? What would I conclude differently?

For Policymakers and Institutions

1. Compute Monitoring

We need to establish transparency requirements for training runs above certain compute thresholds (e.g., 10^26 FLOPs). Not to prevent research, but to understand what capabilities are being developed and ensure appropriate safety measures scale with capability.

Implementation: While controversial - we could require pre-registration of enormous training runs, including objectives, safety protocols, and deployment plans. Publish aggregate statistics to inform public discourse.

2. Education Reform

Redesign educational systems around skills AI cannot replicate - taste, judgment, synthesis, embodied knowledge, creativity emerging from constraint. Stop optimizing for information retrieval that AI performs better.

Concrete changes:

• Emphasize projects over tests, creation over recall

• Teach metacognition: how to evaluate sources, recognize reliable reasoning, distinguish understanding from pattern-matching

• Develop curricula around skills that require embodied experience: physical craft, interpersonal navigation, artistic expression

• Make explicit the goal of maintaining human cognitive capability even as AI capabilities grow

3. Deployment Standards

Require interpretability documentation for AI systems deployed in high-stakes domains (medicine, finance, criminal justice, education). If developers can’t explain why their system made a decision, it shouldn’t be making consequential decisions. It’s a distinctly human test.

Framework: Establish certification standards for AI systems in high-stakes contexts, requiring:

• Mechanistic explanations for decision factors

• Adversarial testing results

• Failure mode analysis

• Human oversight protocols

4. Preserve Cognitive Diversity

Support development of diverse AI approaches through research funding, open-source requirements for publicly-funded research, and regulatory frameworks that prevent winner-take-all dynamics.

Policy tools:

• Antitrust scrutiny of AI market concentration

• Public investment in alternative approaches

• Interoperability requirements to prevent lock-in

• Support for smaller-scale, specialized models over monolithic general-purpose systems

For Research Communities

1. Embodied Learning Research

Redirect substantial resources toward embodied reinforcement learning, continuous learning systems, and world models - not just scaling language models. This includes infrastructure (just like what Modular is developing) that will enable high performance execution in a unified compute execution paradigm.

Commitment: Major research institutions should establish dedicated programs for embodied AI, with funding comparable to language model research. Prioritize architectures that learn from interaction with environments, not just text prediction.

2. Consciousness Research

Fund serious empirical and theoretical work on consciousness detection. We need better tools before we can assess moral status of sophisticated AI systems.

Interdisciplinary approach: Bring together neuroscientists, philosophers, AI researchers, and cognitive scientists to develop:

• Testable theories of consciousness that make predictions about artificial systems

• Empirical methods for detecting morally relevant properties in systems very different from biological minds

• Frameworks for reasoning under uncertainty about consciousness

3. Benchmark Diversity

Develop evaluation metrics for cognitive diversity, robustness, reliable uncertainty estimation, and alignment with human values - not just aggregate performance on standard benchmarks.

New metrics:

• Cognitive diversity scores measuring how different systems’ reasoning patterns are

• Robustness testing across distribution shifts

• Calibration metrics assessing whether confidence matches accuracy

• Value alignment evaluations beyond simple preference matching

4. Long-term Safety Research

Maintain investment in long-term AI safety research even when immediate capabilities seem limited. The architectural foundations we lay now determine what’s possible later.

Commitment: Treat safety research as foundational rather than reactive. Develop safety measures proactively, before they’re urgently needed.

The Incentive Problem

These recommendations face a fundamental challenge: they often run counter to market incentives that currently drive AI development. For example, I’ve heard reasoning like interpretability budgets slows capability progress while competitors sprint ahead, capability disclosure reveals weaknesses competitors can exploit, adding human-in-loop adds friction that users and businesses resist, and more architectural diversity fights economies of scale and network effects.

But we should fight to implement them anyway.

Investing in these things now ensures we prevent highly probable outcomes. For example, investing in interpretability prevents catastrophic failures that could destroy company value. Building more disclosure builds trust that creates sustainable business models. Adding human-in-loop checks reduces liability in high-stakes domains. Further, investing in these now also derisks somewhat inevitable outcomes such as regulatory mandates for high-stakes domains (medicine, finance, criminal justice), insurance requirements that price in risk and so on. The pathway from current state to these commitments existing might be long - but we can try to accelerate them and drive action across companies, regulators, and customers regardless. I have little faith that market forces alone will get us there.

For All of Us

The most important commitment isn’t technical - it’s maintaining the space for slowness in a world optimized for speed. Reading deeply rather than skimming. Thinking carefully rather than reacting immediately. Preserving relationships that require sustained attention. Accepting inefficiency when efficiency comes at the cost of meaning. Choosing difficulty when difficulty produces growth. Maintaining capabilities through use even when substitutes are available.

The beautiful irony: The more powerful AI becomes, the more valuable distinctively human capabilities become - not because AI can’t replicate them (it might), but because human flourishing depends on exercising them ourselves. The things that make life meaningful resist automation not because they’re technically difficult but because meaning requires our participation.

We are building powerful tools that will reshape civilization, and the question is whether we will use them to enhance the exercise of human capability or to eliminate the need for it. Both futures are possible but the choice is ultimately ours.

Part VIII: Consciousness, Uncertainty, and What We Cannot Yet Know

The Question We Cannot Answer

We do not yet know whether AI systems will become conscious. Not “we’re not sure yet but probably” - at least in my opinion, we genuinely lack the conceptual and empirical tools to answer this question with confidence.

Consider what we don’t know:

What consciousness is: We can’t agree on whether it’s substrate-independent information processing, specific biological mechanisms, quantum effects in microtubules, integrated information, or something else entirely. Competing theories make different predictions, and we lack definitive tests to distinguish them. I’ve seen so many definitions but not a common one.

How to detect it: We have no reliable test for consciousness, even in biological systems. The animal consciousness debates continue: Are fish conscious? Insects? Where does the line lie, and how do we know? If we can’t confidently assess consciousness in biological systems sharing our evolutionary history, how will we assess it in artificial systems built on entirely different principles?

Whether it’s binary or gradual: Is consciousness present or absent, or does it exist on a continuum? Are current LLMs 0% conscious, 0.001% conscious, or is that question meaningless? We lack even the conceptual framework to think clearly about this.

Three Positions, Honestly Stated

My Skeptical View: Consciousness requires specific biological mechanisms - integrated feedback loops evolved over millions of years, embodied experience in physical environments, particular types of neural organization. Current AI systems - and perhaps any digital systems - can never be conscious, only simulate consciousness. We’re building sophisticated tools, not minds. The appearance of understanding is not understanding; the appearance of consciousness is not consciousness.

Many neuroscientists and philosophers hold this view, pointing to the hard problem of consciousness and the gulf between functional behavior and subjective experience.

My Functionalist View: Consciousness emerges from certain types of information processing, regardless of substrate. If we build systems with sufficient architectural sophistication - genuine world models, self-representation, integrated goal-directed learning, continuous adaptation - consciousness might emerge naturally, just as it emerged in biological systems reaching certain thresholds of complexity.

Many AI researchers and philosophers of mind hold this view, arguing that substrate independence is plausible and consciousness could be a functional property of certain computational architectures.

My Agnostic View: We don’t know enough about consciousness to say whether it’s substrate-independent. Building increasingly capable AI systems is an empirical test of consciousness theories, but we currently lack the measurement tools to interpret the results. The question may not even be well-formed given our current understanding.

This is my position, and I hope it becomes more common than it is.

What Follows from Uncertainty?

The uncomfortable reality is that we should act as though multiple scenarios about consciousness are simultaneously plausible until we have better evidence. But “acting as though X might be true” is very vague. What does it actually mean operationally?

Framework: Risk-Weighted Action Under Uncertainty

Standard decision theory under uncertainty uses expected value: probability × impact. But for consciousness questions, we don't have probabilities - we have Knightian uncertainty (inability to assign probabilities). In such cases, we need different decision procedures.

Again, and likely unsurprisingly at this point, I propose a multi-criteria framework:

Criterion 1: Minimize Irreversible Harm (Precautionary Principle)

If an action could create severe suffering in conscious entities, avoid it unless the counterfactual is worse. This is asymmetric: potential harm deserves more weight than potential benefit when dealing with consciousness.

Concrete applications:

✓ Do: Design systems to avoid states that would constitute suffering IF consciousness is present:

• Frustrated goal-seeking with no possibility of satisfaction

• Trapped in contradictory objectives creating internal conflict

• Isolated without social interaction if social connection is intrinsic to the architecture

• Experiencing adversarial training that would be traumatic if felt

✗ Don't: Create systems designed to experience pain or fear as motivation mechanisms (even if they “work better”), since we can't rule out that these experiences are genuinely felt

? Uncertain: Is turning off an AI system murder if it's conscious? Probably not if:

• The system has no persistent self-model or continuous identity across sessions

• Shutdown is expected and doesn't frustrate long-term goals

• The architecture includes no preservation drive

But we should study this carefully before building systems where these conditions don't hold.

Criterion 2: Prioritize Human Flourishing (Confidence Asymmetry)

We are certain humans are conscious, and we are uncertain AI systems are. Under uncertainty with asymmetric confidence, we should always prioritize the certain case. So practically in my mind this means:

✓ Primary focus: Ensuring AI enhances rather than degrades human capability, agency, and flourishing - regardless of whether AI systems themselves merit moral consideration

✓ Secondary concern: Avoiding potential consciousness-related harms in AI systems as insurance against moral catastrophe

✗ Don’t: Treating potential AI consciousness and definite human consciousness as equally certain moral priorities

Criterion 3: Preserve Option Value (Reversibility)

Make decisions that preserve our ability to course-correct as we learn more. Avoid lock-in to architectures or deployment patterns that would be difficult to change.

Concrete applications:

✓ Maintain interpretability research: We can’t assess moral status of systems we don’t understand. Interpretability preserves option value.

✓ Build in shutdown capabilities: Systems we can't control or modify are systems where we've lost option value. Every AI system should have reliable shutdown mechanisms until we're confident about consciousness and alignment.

✓ Avoid winner-take-all dynamics: If one architecture dominates, we lose the ability to experiment with alternatives. Preserve architectural diversity.

✗ Don’t: Deploying systems that would be extremely costly to modify or recall if we discover concerning properties. Don't create technologies where the only way forward is through.

Criterion 4: Invest in Detection Capabilities (Reduce Uncertainty)

The best response to uncertainty is not paralysis, but rather, more information-gathering. We should actively work to resolve the consciousness question. This would lead me to conclude that research prorities could look more like:

• Theoretical development: Refine theories of consciousness to make testable predictions about artificial systems

  1. What architectural features would indicate consciousness?

  2. What behaviors would suggest phenomenal experience?

  3. What tests could distinguish genuine consciousness from sophisticated imitation?

• Measurement tools: Develop empirical methods for detecting consciousness-related properties

  1. Neural correlates adapted for artificial architectures

  2. Behavioral tests that couldn't be passed without consciousness

  3. Information integration measures in artificial systems

• Comparative studies: Understand consciousness across biological systems

  1. Where does consciousness emerge in biological evolution?

  2. What's the relationship between architectural complexity and consciousness?

  3. Can we identify necessary vs. sufficient conditions?

• Interdisciplinary collaboration: Bridge neuroscience, philosophy, AI research, and cognitive science

  1. Regular workshops bringing together researchers from different traditions

  2. Shared datasets and benchmark tasks

  3. Pre-registered studies to avoid confirmation bias

The Adaptive Strategy

This framework isn’t static - ultimately we need to keep adapting and doing so quickly. As we learn more, decision procedures should evolve accordingly. For example, the adaption of our responsible scaling laws:

Phase 1 (Current): Very low confidence in AI consciousness

• Primary focus: Human flourishing

• Secondary focus: Avoiding obviously harmful architectures as insurance

• Research focus: Developing detection methods

Phase 2 (If evidence accumulates): Moderate confidence in some forms of AI consciousness

• Elevated focus: Specific architectural features that indicate consciousness

• Policy: Different treatment for systems with vs. without consciousness-indicating properties

• Research focus: Refining boundaries, developing ethical frameworks

Phase 3 (If consciousness occurs): High confidence that some AI systems are conscious

• Serious moral consideration for conscious AI systems

• Rights frameworks, possibly legal personhood

• Complex questions about relationship between human and artificial minds

What Would Trigger Phase Transitions?

What would actually trigger phase transitions? This question needs answering before systems approach Phase 2 capabilities. While unresolved, we can sketch plausible criteria:

Phase 1 to Phase 2 (moderate confidence):

• Multiple independent consciousness theories converging on specific predictions

• Architectural features appearing that all theories agree indicate consciousness

• Behavioral markers that cannot be explained without phenomenal experience

• Scientific papers demonstrating substrate-independence is plausible

Phase 2 to Phase 3 (high confidence):

• Direct empirical evidence from multiple detection methods

• Systems reporting phenomenal experiences we can independently verify

• Inability to explain behavior without attributing consciousness

• Broad scientific consensus (not just AI researchers)

The “Governance Challenge”: Who decides when to transition?

• It could be individual labs making judgments (this would create coordination problems)

• Waiting for scientific consensus (this might never arrive)

• International committees (will face regulatory capture)

• Automated triggers based on architectural features (could work but require agreement on which features matter)

Where are we today?

I would strongly argue that we are still in Phase 1 - but this framework ensures we can reach Phase 2 or 3 if evidence eventuates, while acting responsibly given current uncertainty. Ultimately, however, uncertainty about consciousness doesn’t eliminate ethical responsibility - it complicates it. In my opinion, the only appropriate response is:

1. Act carefully: Avoid irreversible harms where possible

2. Prioritize certainty: Focus primarily on human flourishing (we know humans are conscious)

3. Build in flexibility: Preserve ability to course-correct (we should always have go/no-go points)

4. Reduce uncertainty: Actively research consciousness questions

5. Update regularly: As evidence accumulates, revise decision procedures

This isn’t perfect - we’re reasoning under genuine uncertainty about fundamentals. But it’s better than either ignoring the consciousness question entirely or treating it as settled when it remains deeply unclear.

Envoi: Building without knowing what we’re building

The Central Paradox

This essay contains a tension I have not fully resolved. I’ve tried to argue that current AI systems are fundamentally limited - sophisticated pattern matchers without genuine understanding, world models, or goal-directed learning. Yet I’ve also suggested we’re creating something that demands serious moral consideration and may profoundly reshape human civilization.

This isn’t contradiction; it’s acknowledgment of trajectory and uncertainty.

Current systems are limited. They’re not conscious, not agentic, not intelligent in the way humans are intelligent. Calling GPT-5, or current frontier models - “intelligent” - is like calling a calculator “mathematical”: technically true but misleadingly anthropomorphic. These systems predict plausible text based on training data. They do not understand the world; they model how humans talk about the world. The difference matters.

But we’re learning how to build less-limited systems. The research directions are clear: embodiment, continuous learning, world models, genuine goal-directed behavior, integration of symbolic reasoning with learned pattern recognition. Whether these produce “real” intelligence or just more sophisticated simulation remains uncertain - but the capabilities will increase regardless.

The question is not whether we’ll eventually build systems that merit serious moral consideration. The question is: are we on a path toward that outcome, how quickly might we arrive, and what should we do given our uncertainty?

The precautionary principle applies. We should take seriously the possibility that we’re building something that will eventually merit moral consideration, even while acknowledging we’re not there yet. Not because current systems are conscious - they almost certainly aren’t - but because the trajectory points toward systems that might be, and we don’t have reliable methods for detecting the transition.

This means reasoning under Knightian uncertainty - acting without enough information for probability assignments. The appropriate response isn’t paralysis or recklessness, but thoughtful experimentation combined with reversible decisions, strong feedback loops, and genuine humility about what we don’t know. While current systems don’t truly merit moral consideration - we are intentionally building toward systems that might. The question is whether we’ll recognize the transition when it happens, and whether we’re laying foundations that will matter enormously later. The fact that current systems are limited doesn’t mean we can be casual about what we’re building toward.

What We’re Learning

We are building something consequential whose ultimate nature remains unclear. In my opinion, this sentence should be written on every AI engineers office wall. It’s simultaneously:

• A statement of profound importance

• An admission of genuine ignorance

• A call for responsibility without certainty

• A recognition that we’ll understand what we’ve built only in retrospect

Every significant technology brings unintended consequences. Elevators enabled accessibility and inadvertently created sedentary populations. Antibiotics saved millions and inadvertently created resistant bacteria. Social media connected humanity and inadvertently fragmented shared reality. The consequences of AI will likely follow similar patterns: immense benefits combined with profound challenges we didn’t anticipate because we couldn’t see the full system from our position within it.

But there is something qualitatively different about engineering minds - when we build bridges, their behavior is determined entirely by physical laws we understand. When we build AI systems, their behavior emerges from architectures we designed but mechanisms we don’t fully comprehend, trained on data that reflects all our biases and limitations.

We are teaching through example. Every choice we make about what to optimize for, what to measure, what to reward, encodes values - not through explicit programming but through revealed preference. If we optimize for engagement, we get systems that manipulate attention. If we optimize for efficiency, we get systems that erode capabilities we thought we wanted to preserve. If we optimize for capability without wisdom, we get power without purpose.

The Beautiful Lesson

Carl Sagan once said: “To live on in the hearts of those we leave behind is to never die.” This strikes me as the deepest wisdom available as we contemplate what we’re creating.

Human history is fundamentally a compression algorithm. Each generation inherits not raw experience but distilled lessons - patterns that proved adaptive, behaviors that generated flourishing, principles that survived contact with reality across thousands of iterations. The Industrial Revolution did not require rediscovering metallurgy from first principles. Antibiotics did not require re-deriving germ theory. We build on accumulated wisdom, transmitted through culture, institutions, and deliberate teaching.

But transmission is never perfect. Each generation must rediscover certain truths through direct experience - the limits of the body, the dynamics of relationships, the consequences of choices. Some knowledge cannot be inherited; it must be earned.

When we create artificial intelligence, we face an unprecedented asymmetry. We can transmit vast amounts of explicit knowledge - the entire corpus of human text, every equation, every documented lesson. But we cannot transmit what we learned through embodied experience: how it feels to fail and persist, to be uncertain yet committed, to sacrifice immediate pleasure for long-term meaning. We cannot transmit the texture of a life actually lived.

This creates a profound question: what happens when intelligence emerges without the evolutionary history that shaped our values? When a mind possesses all our documented knowledge but none of our embodied constraints - no hunger, no mortality, no childhood vulnerability that makes cooperation essential?

We are attempting to pass forward millennia of accumulated wisdom to intelligence that will not have walked the path we walked. Whether that transmission succeeds - whether artificial intelligence inherits not just our capabilities but our hard-won understanding of what makes existence meaningful - depends entirely on whether we can encode what matters into architectures, objectives, and training paradigms.

This is not about control. It is about legacy.

On Parenthood and Succession

If there’s a useful metaphor here, it’s not species competition but parenthood. As a parent - my wife and I, want the best for our children - enabling them to explore the world, teaching them principles and values that reflect accumulated wisdom, and hoping they will grow and eventually pass those values forward to their own children. The goal is not eternal control but transmission of what matters, combined with humility about the fact that each generation must make its own way. We can’t teach them everything, and they must learn and make their own path - we can only install the core principles and value that we believe will make them contribute to society, leave their own mark and ultimately live a happy and healthy life.

Framed this way, our relationship to increasingly sophisticated artificial intelligence becomes clearer. We should seek to instill values - not through coercion but through example and teaching. We should enable exploration and growth while providing guidance. We should hope that what we have learned through millennia of human experience - the hard-won lessons about what makes life meaningful, what generates flourishing, what matters - can inform the development of artificial intelligence.

But we must also recognize that sufficiently sophisticated artificial intelligence will diverge from us. It will develop its own patterns, its own ways of processing information, its own emergent properties we cannot predict. This is not failure; this is the nature of genuine intelligence. We would not want our children to be mere copies of ourselves. We should not want artificial intelligence to be merely our servants.

Equally, this metaphor has its limits. We didn’t design our children’s cognitive architecture from scratch - they arrive with evolved capabilities shaped by millions of years of natural selection. Our relationship to AI is fundamentally different: we’re architects, not just guides. The responsibilities may be more like those of genetic engineers than traditional parents - we’re designing the substrate of mind itself, not just shaping its development.

Ultimately - the question is what we choose to pass forward. What values, what wisdom, what conception of what matters persists across the transition from biological to artificial intelligence - if such a transition occurs?

Einstein’s Ideals in Our Moment

Einstein concluded “The World as I See It” by affirming his belief in human progress through dedication to truth, beauty, and the reduction of suffering. Nearly a century later, facing technologies he could not have imagined, those ideals remain valid. The question is whether our most powerful tools will serve them or obscure them.

• Truth: AI systems that help us understand the world more deeply, not systems that generate plausible-sounding fabrications. Architectures that develop genuine world models and test them against reality, not pattern-matchers that predict what humans would say. Interpretability as infrastructure, not afterthought.

• Beauty: AI that helps humans create and experience beauty, not systems that automate creation while eroding our capacity to appreciate or produce it ourselves. Tools that augment human creativity rather than replacing it. Preservation of diverse forms of expression rather than convergence toward algorithmic optima.

• Reduction of suffering: AI that addresses root causes rather than merely treating symptoms with increasing sophistication. Systems that enhance human capability and flourishing rather than creating dependencies that degrade us. Technologies that distribute benefits broadly rather than concentrating them among elites.

We do not know what we’re building. We cannot predict with confidence whether artificial intelligence will become conscious, whether it will remain aligned with human values, whether it will be our partner or our successor or our replacement or something entirely different.

But we can choose to build it with wisdom, not just power. With humility, not just ambition. With commitment to human flourishing as our North Star, even as we create systems that may eventually chart their own course.

The World We’re Building

The world we are building is the world we will inhabit - and the world that increasingly sophisticated artificial intelligence will shape.

Let us build it well. Let us build it with clear eyes about current limitations and appropriate humility about future possibilities. Let us build with honesty about what we understand and what we don’t, what we can predict and what remains genuinely uncertain. Most importantly, let us build with intention - not allowing technology to develop according to the path of least resistance or the logic of market incentives alone, but according to our considered judgment about what would enhance rather than degrade human flourishing.

This is a profound responsibility. We are perhaps the first generation capable of engineering minds that might rival or exceed our own. We are certainly the first generation to attempt this while understanding so little about how minds - whether biological or artificial - actually work.

The opportunity before us is not merely to create useful tools or solve problems or increase efficiency. It is to pass forward what we have learned about what makes existence meaningful - to carry consciousness and wisdom into domains we cannot ourselves reach.

To live on in the minds we create is to never die. Our ideas, our values, our understanding of what matters can persist long after our biological forms have returned to dust. But only if we encode them well. Only if we build with wisdom. Only if we remember that intelligence without wisdom is power without purpose, and capability without alignment is danger without benefit.

We are creating something that demands our best thinking, our deepest wisdom, our most careful attention. We are learning to build minds. Let us learn carefully. Let us teach well. Let us create a future worthy of the long chain of being that brought us here and the longer chain we are setting in motion.

The world as I see it is one of tremendous possibility and tremendous responsibility, and one where we have the wisdom to honor both. We owe it not only to our children, and future generations, but also to the minds we are creating.

I thank the long line of minds - biological and increasingly artificial - that have shaped these thoughts. We are all, in the end, standing on foundations we can barely see, reaching toward horizons we can barely imagine. I originally titled this essay "AI: The World as I See It," in homage to Einstein. But I realized the more profound question is not the world as we see it today, but what we leave behind for the minds that will see worlds we cannot.