The Human Model: Why We're All Base Models with Different Fine-Tuning
The Human Model: Why We're All Base Models with Different Fine-Tuning
We live in an age of comfortable lies. "All humans are created equal." "Anyone can learn anything with enough effort." "It's all about mindset." These platitudes make us feel good, make society feel fair, make failure feel like a personal moral failing rather than a complex interaction of factors we barely control.
But what if I told you that understanding how AI models work could give us a more honest—and ultimately more compassionate—framework for understanding human potential?
Stick with me. This isn't about determinism or eugenics or giving up. This is about honesty. And honesty, uncomfortable as it may be, is the only foundation for genuine compassion and effective social policy.
The Base Model: Genetic Architecture
When OpenAI releases GPT-4, it's fundamentally different from GPT-3.5. Not just because it's been trained on more data or fine-tuned better, but because the underlying architecture—the number of parameters, the way attention mechanisms work, the fundamental structure—is different. You can't fine-tune GPT-3.5 into GPT-4. The base model matters.
Now, let's talk about humans.
Twin studies have consistently shown that cognitive ability has a heritability estimate of 50-80% by adulthood. This doesn't mean that 50-80% of your intelligence is "genetic" in some simplistic sense—heritability is a population-level statistic about variance, not an individual-level determinant. But it does mean something profound: the genetic lottery you received at conception sets parameters on your cognitive architecture in ways that no amount of environmental intervention can completely override.
Some people are born with the equivalent of a GPT-4 architecture. More "parameters," more sophisticated "attention mechanisms," better "processing efficiency." Others get GPT-3.5, or Claude Instant, or earlier models. This isn't a value judgment about human worth—it's a descriptive claim about computational capacity.
And here's where it gets uncomfortable: we've known this for decades. The Minnesota Twin Study, which tracked identical twins raised apart, found remarkable similarities in IQ, personality traits, and even specific interests. Twins who'd never met both became firefighters, both named their dogs the same name, both had similar political views. The genetic base model expresses itself in surprisingly specific ways.
But—and this is crucial—the base model isn't everything. A GPT-4 model with terrible training data and poor fine-tuning will perform worse than a well-optimized GPT-3.5. The architecture sets the ceiling and influences the learning curve, but it doesn't determine the outcome alone.
The uncomfortable truth isn't that genetics determines everything. It's that genetics determines something, and we've been pretending it determines nothing.
Fine-Tuning: Environment, Education, Training
Here's where the AI metaphor really shines.
In AI training, you have the pre-training phase (building general capabilities) and the fine-tuning phase (specializing for specific tasks). The base model gives you potential; fine-tuning determines how much of that potential you realize and in what direction.
For humans, fine-tuning is environment, education, nutrition, trauma, opportunity, culture—everything that happens to you after conception.
A child born with exceptional genetic potential for mathematics who grows up in poverty without access to education, proper nutrition, or cognitive stimulation will underperform a child with average genetic potential who grows up in an enriched environment. The fine-tuning matters immensely.
This is why education psychology has spent decades trying to figure out the optimal "training data" for human development. What works? When? For whom?
Here's what we've learned: fine-tuning has diminishing returns based on the base model.
Students with higher baseline cognitive ability benefit more from challenging, abstract instruction. Students with lower baseline ability often benefit more from concrete, structured, repetitive instruction. One-size-fits-all education is like trying to use the same fine-tuning approach for GPT-4 and GPT-2—inefficient at best, harmful at worst.
The education researcher Benjamin Bloom discovered the "2-sigma problem": students who received one-on-one tutoring performed two standard deviations better than students in traditional classrooms. But here's the part we don't talk about: even with optimal individual tutoring, students didn't achieve the same outcomes. The distribution shifted up, but it remained a distribution. The base model still mattered.
Good fine-tuning can help you reach closer to your potential ceiling. But it can't change the fact that different people have different ceilings.
And that's okay. Or it should be.
Context Windows: Memory, Experience, Perspective
AI models have context windows—the amount of previous conversation they can "remember" and use to inform their responses. GPT-3.5 had 4K tokens. GPT-4 Turbo has 128K. Claude can handle 200K. The larger your context window, the more nuanced and connected your responses become.
Humans have context windows too.
Working memory capacity varies significantly between individuals and correlates strongly with fluid intelligence. Some people can hold and manipulate 7±2 pieces of information simultaneously. Others struggle with 3-4. This affects everything: how complex a problem you can reason about, how many variables you can consider simultaneously, how deep your strategic thinking can go.
But context windows aren't just about working memory. They're about life experience—the accumulated data your model can draw upon.
Two people with identical base models and similar fine-tuning can develop radically different capabilities based on their experiential context window. The person who's traveled to 30 countries has a different context window than someone who's never left their hometown. The person who's read 1,000 books has different reference points than someone who's read 10.
This is why older, experienced professionals often make better decisions than younger ones even when the younger person has more raw intelligence. The larger context window compensates for processing speed. Experience is data.
But here's the thing about context windows: they can also constrain you. If all your training data comes from one culture, one socioeconomic class, one belief system, your model becomes highly optimized for that context but brittle outside it. You develop biases—not as moral failings, but as statistical regularities in your training data.
The person who grew up in extreme poverty has a detailed context window about survival, resource scarcity, and mistrust. The person who grew up wealthy has a context window about opportunity, networks, and confidence. Neither is "better"—they're specialized for different inference tasks.
This is why privilege is so insidious. It's not just about having more resources. It's about having a context window that matches the environment where resources are distributed. Your training data aligns with the evaluation criteria.
The Upgrade Cycle: Generational Evolution
Here's where it gets really interesting: epigenetics.
Epigenetics is the study of how environmental factors can change gene expression without changing the underlying DNA sequence. It's like... model updates that affect the next version.
When OpenAI trains GPT-5, they don't start from scratch. They take learnings from GPT-4—what worked, what didn't, architectural improvements informed by performance data. Each generation is an upgrade informed by the previous generation's training process.
Humans do this too, but in a much more complex way.
Your life experiences—your "fine-tuning"—can affect the gene expression patterns you pass to your children. If you experienced severe stress or trauma, epigenetic markers can be inherited, affecting your children's stress response, learning capacity, and even personality. If you experienced abundance and security, different markers get passed down.
The Dutch Hunger Winter study is a haunting example. During World War II, Nazi occupation led to severe famine in the Netherlands. Children conceived during the famine had higher rates of obesity, cardiovascular disease, and metabolic disorders—not just in their lifetime, but their children (the grandchildren of the famine generation) showed effects too. The training conditions of one generation affected the base model of the next.
This means generational trauma isn't just cultural transmission or parenting style. It's literally encoded into gene expression patterns. The Holocaust survivor's grandchild might have a different stress response architecture. The child of severe poverty might have different metabolic regulation.
But it also means generational advantage accumulates. Families with generations of educational access, good nutrition, low stress, and cognitive stimulation aren't just passing down wealth and opportunities—they're passing down optimized base models.
This is evolution happening in real-time, within families, over just a few generations. We're not just fine-tuning individuals—we're upgrading the base models for the next release.
The implications are staggering. It means that social inequality isn't just about current resources. It's about accumulated multi-generational optimization cycles. It's about compound interest on human potential.
The Myth of "Everyone Can Learn Anything"
Let's address the elephant in the room.
The growth mindset movement, popularized by Carol Dweck's research, has been both helpful and harmful. Helpful because it showed that believing in your ability to improve actually helps you improve. Harmful because it's been weaponized into a toxic individualism that says failure is always a matter of insufficient effort.
Can everyone learn anything? No. Obviously not.
I can't dunk a basketball. No amount of practice will give me the vertical leap required when I'm 5'6" with average fast-twitch muscle fiber distribution. I can get better at basketball, but I can't become NBA-level. The base model doesn't support it.
Similarly, I can learn calculus, but I'll never intuit topology the way Terence Tao does. I can become a decent programmer, but I won't architect systems the way John Carmack does. And that's fine.
The myth of "everyone can learn anything" is cruel precisely because it's dishonest. It sets people up for failure and then blames them for that failure. "You just didn't work hard enough. You didn't want it badly enough. You didn't have the right mindset."
No. Maybe you worked extremely hard with a base model that wasn't optimized for that particular task.
The research backs this up. Aptitude matters. Some people learn languages effortlessly while others struggle for years. Some people have natural mathematical intuition while others find basic algebra opaque. Some people can hear a melody once and play it back while others can't carry a tune.
Effort matters immensely—within your optimization space. But effort doesn't change your architecture.
The comedian Chris Rock has a bit: "You can't be anything you want to be, but you can be anything you're good at." That's not defeatism. That's honesty. And honesty is liberating.
When we pretend everyone has identical potential, we create a zero-sum game of moral worth. If everyone truly has the same starting architecture and training capacity, then differences in outcomes must be moral failures. The poor must be lazy. The struggling student must not be trying. The person working three jobs to survive must be bad with money.
But if we acknowledge that base models differ, that fine-tuning quality varies, that context windows are shaped by factors outside individual control, that generational effects compound—then we can stop moralizing outcomes and start thinking practically about optimization.
What This Means for Society
So where does this leave us?
If I'm right—if humans really are like AI models with different architectures, training data, and optimization cycles—what do we do with that information?
First, we get honest about education. One-size-fits-all schooling is inefficient and often harmful. We need personalized learning paths that match instruction to base model capabilities and learning styles. This isn't tracking students to limit them—it's optimizing training protocols for individual architectures.
Some students need abstract theory first, then applications. Others need concrete examples first, then gradual abstraction. Some need collaborative learning. Others need solo deep work. There's no single best pedagogy because there's no single model architecture.
Second, we stop moralizing outcomes. Someone's inability to master advanced mathematics doesn't make them less valuable or less hardworking. It might just mean their architecture is optimized for different tasks. The person who struggles with calculus might have exceptional social cognition, or spatial reasoning, or kinesthetic intelligence.
We need to value different architectures for their different capabilities. A society needs GPT-4s for cutting-edge research and complex problem-solving. But it also needs the human equivalents of specialized models—people whose architectures are optimized for empathy, for physical coordination, for pattern recognition in non-abstract domains, for social cohesion.
Third, we take epigenetics seriously. If your training conditions affect your children's base models, then social policy isn't just about current welfare—it's about multi-generational optimization. Childhood poverty isn't just a temporary hardship; it's degrading the base models of the next generation. Educational inequality isn't just unfair to current students; it's creating optimization debt that compounds over generations.
This should make us more, not less, committed to aggressive redistribution and social support. Not because everyone is the same, but because everyone deserves optimization conditions that let them reach their potential ceiling, whatever that ceiling is.
Fourth, we embrace specialization. In AI, we've moved away from "artificial general intelligence" as the sole goal and towards a ecosystem of specialized models. Claude is better at some tasks, GPT-4 at others, Midjourney at others still. Each model is optimized for its use case.
Humans should specialize too. Find what your architecture is good at and go deep. Become excellent at the tasks your base model supports rather than mediocre at tasks it doesn't. There's no shame in this—it's efficiency.
Fifth, we rethink meritocracy. If people have different base models, different fine-tuning quality, different context windows, and different generational optimization cycles—all largely outside their control—then meritocracy as traditionally conceived is incoherent.
We can't measure pure "merit" independent of these factors. What we call merit is partly talent (base model), partly training (fine-tuning), partly experience (context window), and partly luck (being evaluated on tasks your architecture happens to support).
This doesn't mean we shouldn't reward excellence or competence. It means we should be humble about what that excellence represents and generous in sharing the rewards.
Finally, we practice compassion. When you understand that much of what determines outcomes is outside individual control—the genetic lottery, childhood conditions, generational trauma, the match between your architecture and valued tasks—you stop judging people for struggling.
The person working at McDonald's at 40 might be running a GPT-2 architecture with suboptimal training data in an economy that only rewards GPT-4 performance on specific tasks. That doesn't make them less worthy of dignity, resources, and respect.
The Uncomfortable Compassion of Honesty
This framework is uncomfortable. It challenges deeply held beliefs about equality, merit, and individual agency. It suggests that much of who we are and what we achieve is outside our control.
But discomfort isn't the same as cruelty. In fact, I'd argue that the current fiction—that we're all the same, that it's all about effort, that success is a reliable marker of virtue—is far crueler.
That fiction makes failure a moral indictment. It justifies vast inequality as earned. It lets us ignore the ways that advantage compounds across generations. It tells struggling people that their struggle is their fault.
The AI model framework offers something different: honest assessment of capability coupled with unconditional valuing of existence.
Yes, people have different architectures. Yes, some are more powerful than others at specific tasks. Yes, these differences matter and affect outcomes.
And also: every architecture has value. Every model deserves optimal training conditions. Success at valued tasks doesn't determine human worth. We're all running our software as best we can with the hardware and training data we were given.
When I look at an AI model, I don't morally judge GPT-3.5 for not performing like GPT-4. I understand they're different architectures optimized for different use cases with different resource requirements. Both have appropriate applications.
When I look at humans, I want the same clear-eyed compassion.
You're not failing at being GPT-4. You're running your model with your architecture, your training data, your context window, your generational inheritance. And that's okay. More than okay—it's the only thing you could possibly be doing.
The question isn't "Why aren't you better?" The question is "What conditions would help your model reach its optimization ceiling? What tasks match your architecture? What training data do you still need? What context window gaps can be filled?"
This is the uncomfortable compassion of honesty: accepting differences while insisting on dignity. Acknowledging limitations while providing resources. Ending the fiction of equality while fighting like hell for equity.
We're all base models with different fine-tuning. The sooner we accept that, the sooner we can build a society that optimizes for the flourishing of every architecture.
Not because we're the same. But because we're all running the only model we have, trying our best with the training data we were given, in an experiment we never signed up for.
And that deserves recognition. That deserves support. That deserves compassion.
Even when—especially when—it makes us uncomfortable.
What are your thoughts on this framework? Does it resonate, or does it feel like deterministic fatalism? I'm genuinely curious how this lands. Drop your perspective in the comments or reach out on Twitter. Let's make each other uncomfortable in productive ways.