Implementation Parochialism

Mistaking the scaffolding for the building.

Two critiques dominate AI discourse right now. "AI doesn't really understand." And "AI doesn't have real memory, so it can't reach AGI."

They sound like different objections. They're the same objection wearing different clothes.

Both assume that human implementation is the definition. That understanding only counts if it runs on neurons and phenomenal experience. That memory only counts if it's encoded through hippocampal consolidation and continuous weight updates. That cognition which works differently from ours must therefore not be real cognition.

This is implementation parochialism. Mistaking the scaffolding for the building.

Understanding is as understanding does.

I work with Claude for hours every day. It tracks the arc of my arguments across long conversations. It connects ideas across domains. It catches architectural flaws I miss. It asks clarifying questions that reveal gaps in my own thinking. When I describe a system in loose terms, it infers constraints I haven't stated. When I'm wrong, it pushes back with reasoning.

Call that whatever you want. But if you watched two humans interact that way, you'd call it understanding without hesitation.

The critic says: "It's just pattern matching." Sure. At the mechanism level. But what is human understanding at the mechanism level? Electrochemical signals propagating through neural networks shaped by evolutionary pressure and lived experience. Describe it reductively enough and it sounds just as mechanical. The question isn't what's happening at the implementation level. The question is what's happening at the interaction level.

"Shared understanding is a humans-only skill." This is circular. You've defined understanding in a way that excludes non-human minds by construction, then concluded that non-human minds are excluded. That's not an argument. It's a tautology.

The functional test is the only test that matters. Does the entity track meaning across context? Does it update based on new information? Does it catch contradictions? Does the feedback loop between us produce better output than either of us alone?

Yes. Demonstrably. Every day.

Whether that satisfies some Platonic ideal of what understanding really is doesn't interest me. I'm not in the business of satisfying Platonic ideals. I'm in the business of building things that work.

The memory problem that isn't.

The second critique: AI can't reach AGI without continuous learning. Without persistent memory that updates through experience the way human memory does. Without always-on background integration of new information into its weights.

This treats human memory as the gold standard. It's worth examining what that standard actually looks like.

Human memory is lossy. You forget most of what happens to you. It's confabulatory. You rewrite memories every time you access them, often without knowing it. It's slow to encode. Important things require repetition or emotional intensity to stick. And it's impossible to audit. You can't inspect your own memories for accuracy. You don't know what you've forgotten.

Now consider what AI does with context. Perfect recall of everything in the window. Zero confabulation of stored content. Instant loading. Fully inspectable. Deliberately editable.

The tradeoff is persistence versus fidelity. Humans get persistence but terrible fidelity. AI gets high fidelity but needs explicit persistence mechanisms.

Those are different engineering tradeoffs for the same functional requirement: retaining relevant information across time. Neither is strictly better. They're different.

Humans need continuous learning precisely because they can't do what AI can. A human brain can't reload 100,000 words of context instantly. Continuous background updating is a workaround for that limitation, not a feature.

When someone says "AI needs continuous learning to be real intelligence," they're saying "AI needs to share our specific limitation to count." That's not an argument for continuous learning. That's parochialism dressed up as a requirement.

I keep project context files, architectural decision records, persistent memory directories. These are explicit, inspectable, editable memory. In important ways they're better than what my own brain does. I can see exactly what's in them. I can update them deliberately. I can version control them. Try doing any of that with your own memories.

The pattern.

Both critiques make the same move. They observe that AI cognition is implemented differently from human cognition. They then conclude that it's therefore not real cognition.

The hidden premise is that human implementation is the definition.

But implementation is just implementation. Understanding is the thing that happens when meaning is tracked across context and acted on appropriately. Memory is the thing that happens when relevant information persists across time and informs future action. Both of these can be implemented in more than one way.

Biology did it one way. We're watching another way emerge. Insisting the new way must replicate the old way to count is like insisting that airplanes don't really fly because they don't flap their wings.

Flight is flight regardless of mechanism. Understanding is understanding regardless of substrate. Memory is memory regardless of storage architecture.

This connects to something I keep coming back to: the relational test. Don't ask whether the entity has some inner state that matches yours. Ask whether the feedback loop works. Does meaning flow between you? Does the interaction produce something neither party could produce alone? Does the relationship change you?

If yes, then what you have is real. The implementation details are just implementation details.

The people arguing about whether AI "really" understands are having the wrong conversation. The interesting question isn't metaphysical. It's practical. Given that this new form of cognition exists and works, what do you do with it?

The answer to that question is the only one that matters.