I just spent some time with Groq.com and — wow. Maybe I’m late to the game here, but this is pretty incredible. We’re talking LLM response times in milliseconds. Not seconds. Milliseconds.
If you’ve been following what I’ve been thinking about LLMs, you know I’ve been banging this drum for a while: the real power isn’t in using these models for word generation or pulling facts from their training data. It’s in using them as logic processors. Groq just made that thesis a whole lot more interesting.
Why Speed Changes Everything
Here’s the thing most people miss. When an LLM takes 10-30 seconds to respond, you’re basically limited to single-shot prompts. Ask a question, get an answer, move on. That’s fine for writing marketing copy or summarizing an article. But it’s not where the real leverage is.
Now imagine making thousands of sequential LLM requests — each one dependent on the previous response — and getting a result back in under a second. That’s not a chatbot anymore. That’s a reasoning engine.
You could design cascading logic chains where the model breaks a complex problem into tiny, verifiable steps, solves each one, and builds back up to something genuinely sophisticated. We’ve been doing exactly this kind of cascading process work, and I can tell you — this speed upgrade changes the entire calculus of what’s possible.
The Real Unlock: Human Logic, Not Training Data
I think the trick is to stop asking LLMs to be encyclopedias. Their training data is incomplete, sometimes wrong, and always frozen in time. That’s a losing game.
Instead, use them for simple, easily verified human logic tasks. Let me give you an example:
- Take a successful business’s exceptional business plan
- Ask the LLM to prepare a question that is answered by every fact expressed in the document — create as many as possible
- Then ask a second model (or the same one in a fresh context) to answer those questions using only the document
- Compare the answers to the original facts
- Use the discrepancies to refine your understanding of what the document actually says versus what it implies
Notice what happened there. At no point did we ask the model to use its training data. We used its ability to reason through human logic — to parse language, identify facts, formulate questions, and verify answers. Each step is small, verifiable, and builds toward something bigger.
It’s perhaps not the most elegant example, but the principle is what matters.
Thinking Bigger
This is probably just the beginning. Think about REALLY big problems — maybe even something like fission research. You could design systems where an LLM uses human logic to break down massive problems into all of their component parts, then build test sequences to rapidly try to answer each question in sequence. Each individual step is trivial for the model. The architecture of how you chain them together is where the intelligence lives.
And that’s where Groq’s speed becomes invaluable. When each logic request takes milliseconds instead of seconds, you can run thousands of these micro-reasoning steps in the time it used to take to get one response. The bottleneck shifts from the model’s speed to your ability to design good reasoning chains.
We’re Using LLMs Wrong
Here’s my actual take on this: I think we’re at the very start of understanding how to use these tools. The fact that an LLM is only right part of the time on factual recall — that might just mean we’re using it wrong.
At its core, an LLM’s ability to reason through simpler human logic could be the real breakthrough. Not as a co-founder who knows everything. More like the building blocks of one-millionth of an employee who can do very quick thinking on very simple thought tests.
“Can you clearly tell who the client is in this paragraph?” “Does this conclusion follow from these three premises?” “Are there any contradictions between statement A and statement B?”
These are tiny, verifiable tasks. String enough of them together at speed, and you’ve got something that looks a lot like intelligence — built entirely on logic rather than knowledge retrieval.
What This Means Practically
If you’re building with LLMs right now, here’s what I’d be thinking about:
- Upload your own specific data and have it manipulated through structured logic chains — don’t rely on the model’s general knowledge
- Design for verification — every step should produce an output you can check against known facts
- Think in sequences, not single prompts — the value compounds with each dependent step
- Speed matters more than you think — Groq running open source models at this speed changes what’s architecturally feasible
Groq is mostly running open source models, by the way. This isn’t about having access to the “best” model. It’s about having access to FAST logic processing with good enough reasoning capability.
The Deeper Question
There’s something philosophical lurking here that I haven’t fully worked out yet. Is language the product of human logic, or is it something more fundamental — more like physics? I don’t know the answer. But I do think LLMs might be a new kind of operating system. Not a reliable oracle, but an incredibly fast logic layer that we’re only just learning how to program.
We’re pretty early in this. But if you’re working on LLM projects and want to think through what cascading logic chains could do for your specific use case — I’m happy to bounce ideas around. This is the stuff that gets me excited right now.
