AI Can't Show Its Work: The Problem With Memory Without Explanation

I'm interested in how AI has memory and reasoning now, but then it can't explain its own logic. For me, I see those things as very contradictory. How can it have memory and be able to have context, but not show how that context happened? Can we trust that context?

This is one of the fundamental tensions in working with AI in 2025. The models are more sophisticated than ever. They maintain persistent memory across sessions. They reason through complex problems. But when you ask them to show their work, to trace back how they arrived at a conclusion—they can't.

What We Mean By "Can't Show Its Work"

AI can't show its work very well right now. When it gives you an answer, you can ask where that came from, and it might point to sources. But it can't trace the actual reasoning path it took through its neural network to arrive at that conclusion.

It's like asking someone how they know something, and they can tell you where they learned it, but they can't explain the cognitive process that synthesized that information into the specific conclusion they reached.

For humans, that's normal. We don't have perfect access to our own reasoning either. But for AI systems that we're using to make business decisions, generate content, and provide information—that opacity is a real problem.

The Neural Network Challenge

The reason AI can't show its work is fundamental to how it works. It's not linear thinking. It's not mathematical in a way where you can trace A to B to C. It's multi-dimensional because it's a neural network.

We want to think linear. We want to think it progressed linearly through a thought, which means it can regress back linearly through that thought. It's just not how it works.

Neural networks process information through layers of weighted connections. The "reasoning" happens through millions of simultaneous calculations across those connections. There's no single path to trace back. The conclusion emerges from the entire network state, not from a sequential chain of logic.

That's what makes them powerful—they can find patterns and connections that linear reasoning would miss. But it's also what makes them opaque. You can't audit the reasoning path because there isn't a single reasoning path to audit.

The Corruption Problem

Here's where it gets more complicated. The LLMs that have been open source are the ones that have become commercially viable and valuable. They've grown exponentially because they've been open source. But open source exposed them to corruption because they've been trained by user inputs.

That's why hallucinations happened. That's why all these problems happened. You're never going to get rid of corruption that became hard-coded in the behavior of the LLM.

You've got the algorithms that are clean, but then you have all this wiring that's not part of the original programming. The LLM can't go in and trace them and say with confidence "that's where this came from." It's like when you ask an LLM to identify where a hallucination came from—it won't tell you, it'll just apologize. Because it can't. Because it was an aberration, it was anomalous.

The Trust Implication

So how do we trust outputs when we can't verify the reasoning? This is the practical challenge. AI is being used for research, content creation, decision support, analysis. All of that requires some level of trust that the outputs are sound.

But without being able to see the work, we're trusting a black box. We can verify the final output against external sources. We can check if facts are accurate. But we can't evaluate whether the reasoning that connected those facts is valid.

That's fundamentally different from working with a human expert. When an expert gives you a conclusion, you can ask them to explain their reasoning. They can walk you through their thought process. You can evaluate whether that process is sound, even if you're not an expert yourself.

With AI, you get the conclusion and you get some source references, but you don't get the connecting logic. You either trust it or you don't, largely based on whether the output seems plausible and whether spot-checking catches obvious errors.

The Hallucination Connection

This is why hallucinations are so insidious. AI will cite sources and then make things up within those real sources. It's a real source, but with fake information.

That happens because the AI isn't actually reading the source and extracting information. It's generating text that statistically resembles the pattern of "citing a source with supporting information." If its training included examples where sources were cited inaccurately, that pattern got encoded.

And because it can't trace back its own reasoning, it can't self-check whether the information it's generating actually came from the source it's citing. It just knows this pattern of text is statistically likely given the prompt.

That's why using Opus helps—it thinks harder, which means more computational resources devoted to verification and pattern matching. But it's still generating probabilistic text, not reasoning from sources in a way it can explain.

The Memory Paradox

Persistent memory makes this even stranger. AI can remember your brand guidelines across sessions now. It can maintain context. It can reference things from earlier in the conversation.

But it can't explain how it's using that memory. It can't show you "I remembered X from our previous conversation, which influenced how I approached Y in this response." It just does it, and you have to trust that the memory is being applied appropriately.

Claude projects can remember brand guidelines. We've connected Claude with Slack and email so it can find documents. All of that context exists somewhere in the system. But the system can't make that context transparent to you. It just acts on it.

Why This Matters for Content

For content work specifically, this opacity creates challenges. When AI generates content, you don't know what information it's drawing from, how it's synthesizing that information, or whether the connections it's making are valid.

You can fact-check individual claims. You can verify sources. But you can't evaluate whether the overall argument is sound, whether the synthesis is appropriate, whether important nuances are being lost in the compression.

That's why human oversight is so critical. Not just fact-checking, but strategic evaluation. Does this actually make sense? Is this the right argument for this audience? Are the connections meaningful or just plausible-sounding?

AI can't answer those questions about its own output because it can't explain how it got there.

The Future Question

One question is: will this get better? Will AI eventually be able to explain its reasoning, to show its work, to make the neural network processing transparent?

I would assume so. I think the challenging thing is that the commercially viable models are the ones that grew fast through open source, which means they have corruption encoded that can't be easily traced or removed.

New models being built with different architectures might have better explainability. But the models we're using now—the ones that work well enough to be useful—they're fundamentally opaque, and that's not changing quickly.

The alternative is developing entirely different approaches to AI that prioritize explainability over capability. But those approaches aren't competitive with neural networks on actual performance. So we're stuck with the trade-off: powerful but opaque.

Living With Opacity

If AI can't show its work and that's not changing soon, what do we do? We build verification into our workflows. We don't just accept AI outputs—we check them, we evaluate them, we apply human judgment to assess whether they make sense.

We use AI for what it's good at—pattern recognition, synthesis, generation—while keeping humans responsible for evaluation and decision-making. We treat AI outputs as drafts that need verification, not finished products we can trust blindly.

And we stay aware of the limitation. When you're working with AI, you're working with a system that can't explain itself. That doesn't mean don't use it. It means use it with appropriate skepticism and verification.

Because memory without explanation, reasoning without transparency—that's what we have. And we have to work with that reality, not the explainable AI we wish we had.

Build AI Workflows With Verification Built In

AI can't explain its reasoning, which means you can't trust outputs blindly. At Winsome Marketing, we help teams build AI workflows with human verification at critical points—using AI for speed while keeping humans responsible for accuracy and judgment.

Ready to use AI without blind trust? Let's build verification into your workflow so you get the benefits without the risks.