Thoughts on Open AGI

I spent last weekend wrestling with a 7B parameter model on my old laptop and wow, what a journey. The laptop is probably five years old at this point, definitely not what you'd call cutting edge, and I figured I was setting myself up for frustration. But after about four hours of downloading, fiddling with settings, and watching my fan sound like a jet engine, the thing actually worked. Not blazing fast by any means, but it worked.

It got me thinking about why these small language models are such a big deal right now. The AI headlines are always screaming about the next massive breakthrough from the big labs, but meanwhile there's this quieter revolution happening with models that can actually run on hardware normal people own.

And honestly? For most of what I want to do with AI, the 7B model was plenty good. I was using it to help me reorganize some notes, brainstorm a few ideas for a side project, and clean up some messy text files. Not once did I think "gosh, if only this had 100 billion more parameters." It just did the job. The responses weren't as polished as what you get from the cloud giants, sure, but they were helpful and they happened on my machine, using my electricity, without sending my data anywhere.

That's the thing that really clicks for me about small models. They're not trying to be everything to everyone. They're like that friend who might not know calculus but is excellent at helping you think through everyday problems. Sometimes you don't need the polymath. You just need someone reliable who shows up fast and doesn't cost you twenty bucks in API calls.

The whole weekend experiment also made me realize how much the tooling has improved. A couple years ago, running any decent language model locally felt like you needed a PhD and a server farm. Now you can literally type one command and wait. It's still not iPhone-simple, but it's getting there. And once you get past the initial setup hurdles, there's something really satisfying about having this AI assistant that lives entirely on your computer.

The economics are starting to make sense too. If you're the kind of person who uses AI tools regularly, those monthly subscription fees add up. And if you're working on anything remotely sensitive, keeping everything local just feels smarter. Plus there's no waiting for API responses when your internet decides to be moody. The model is right there, ready to go, even if the power goes out and you're running on battery.

I've been thinking about this question all morning after reading a thread online where someone insisted we already have AGI and someone else insisted we'll never get there. The word that keeps bothering me is "general."

What does it even mean for intelligence to be general? It's such a slippery concept, isn't it? We use it casually, like we all understand what we're talking about. But then I try to pin it down and it becomes this philosophical quicksand.

When I think about human intelligence, the thing that strikes me is how messy and domain-specific it actually is. I can write code but I'm terrible at fixing my car. My neighbor can diagnose engine problems by sound but struggles with basic spreadsheet formulas. Are either of us generally intelligent? We both solved the same college calculus problems twenty years ago, but ask us today and we'd probably stare at you blankly.

And yet there's something undeniably flexible about how we think. The way I approach debugging code isn't fundamentally different from how I figure out why my dishwasher isn't draining properly. There's some underlying capacity to break down problems, form hypotheses, test them. Some ability to transfer insights from one domain to another, even if imperfectly.

Maybe that's what general means. Not that the intelligence works equally well everywhere, but that it has this quality of transferability. The capacity to take something learned in one context and apply it to something completely different. But even that feels incomplete, because humans do this so inconsistently. Sometimes we make brilliant connections across fields. Sometimes we fail to apply the most basic reasoning to areas outside our expertise.

I keep coming back to the question of whether general intelligence is even a real thing or just a useful fiction. Maybe all intelligence is actually narrow, and what we call general is just narrow intelligence that's very good at adapting and combining different narrow capabilities. Like a skilled improviser who can work with whatever they're given, not because they know everything, but because they've learned how to learn quickly.

I was sitting in a coffee shop this afternoon, nursing a cold brew and trying to make sense of some code, when I overheard two engineers at the next table getting into it about open source versus open science. One of them kept saying "open is open," waving his hands around. The other was insisting there was some crucial difference I couldn't quite catch over their raised voices.

It got me thinking. Because honestly, I'd been using these terms pretty interchangeably in my head, especially when it comes to AI. Both sound good, right? Open source, open science. Transparency all around. But after listening to their back-and-forth for twenty minutes, I realized I might be missing something important.

Open source in AI seems pretty straightforward, at least on the surface. You release the code. People can see how the thing works, modify it, build on it. Think about all those models flooding out of research labs lately. The weights get posted, the inference code shows up on GitHub, and suddenly everyone's running these massive language models on their laptops. That's the dream, anyway. Though when you dig deeper, it gets murky fast. What about the training data? What about the compute that went into making the thing in the first place? How "open" is open when only companies with millions of dollars can actually create these models?

Open science feels different to me. Bigger, maybe. It's not just about the final product, but about the whole process. The hypotheses, the failed experiments, the datasets, the methodology. Everything that went into understanding something, not just the polished result. When I think about scientific breakthroughs, they rarely happen because someone got access to a final tool. They happen because someone could trace through the thinking, replicate the work, build on the reasoning.

And here's where it gets interesting with AI. Most of what we call "open source" AI models are really just... the end result. The final weights after training. Which is useful, don't get me wrong. But it's kind of like getting a finished sculpture without seeing the sketches, the failed attempts, the process of learning what worked and what didn't. You can use the sculpture, even copy it, but can you really understand how to make the next one?

Maybe the distinction matters more than I thought. Open source gives you tools. Open science gives you understanding. In AI, we're drowning in tools but starving for understanding. I can download a model that's supposedly as good as anything the big labs have built, but I have no idea why it works, what it's really learned, or how to make it better. That feels like we're missing something essential about how knowledge actually advances.

But then again, maybe I'm overthinking this. Maybe those two engineers were just arguing semantics while the real work happens regardless of what we call it.

I sat at my kitchen table this morning, coffee getting cold, scrolling through comments on yet another post about whether the big AI labs are "close" to something truly transformational. The usual suspects were there. People talking about alignment problems, capability jumps, compute thresholds. But tucked between the technical jargon and breathless predictions, I kept seeing this one word: open.

Open models. Open research. Open development.

I've been thinking about this a lot lately. Not just the technical aspects, though those matter too. But why I find myself drawn to the idea that whatever we're building toward, this artificial general intelligence that everyone seems to agree is coming, should be developed in the open. Maybe it's naive. Maybe it's dangerous. But I can't shake the feeling that closed development, no matter how well-intentioned, is the wrong path.

It's partly about power, I think. The traditional narrative has these well-funded labs racing toward AGI behind closed doors, making unilateral decisions about humanity's future. That sits wrong with me. Not because I think the people running these labs are evil, but because concentrating that much capability in so few hands feels like a historical mistake we should recognize by now. Every time we've had transformational technologies developed in secret by small groups, the results have been... mixed at best. And the downsides have often hit the people who had the least say in how things were developed.

But there's something deeper here too. I was reading about how some of the recent breakthroughs in reasoning and multimodal capabilities emerged from models that were at least partially open. Teams around the world building on each other's work, catching errors, pushing boundaries in directions the original developers never considered. There's this collective intelligence aspect to open development that feels essential for something as complex as AGI. No single organization, no matter how brilliant or well-resourced, can anticipate all the failure modes or beneficial applications.

The counterargument is obvious. AGI could be dangerous. Maybe catastrophically so. Shouldn't we be extra careful about who gets access? I understand the concern. Really, I do. But I'm not convinced that closed development actually makes us safer. Security through obscurity has a pretty terrible track record in technology. And there's something unsettling about the assumption that a small group of people, however smart and well-meaning, should make safety decisions for everyone else without broader input.

I keep coming back to this question: what kind of world do we want to live in after AGI arrives? One where a handful of institutions control the most powerful technology ever created? Or one where that capability is more distributed, more contestable, more accountable to the people whose lives it will reshape? The choice of development approach isn't separate from that outcome. It's how we get there.

Maybe I'm wrong about this. Maybe the risks really are too high for open development. Maybe there are technical reasons why it won't work. But right now, watching the field evolve, I find myself hoping that the open path proves viable. Not just because of what it might produce, but because of what it represents about how we make decisions about our technological future.

I've been reading about artificial general intelligence for a while now. Probably longer than is healthy for someone who isn't a researcher. But the thing that keeps pulling me back isn't the technical papers or the benchmark results. It's the question of openness.

Who gets to build AGI? Who gets to decide how it works? And more importantly, who gets to use it?

Right now, a few well-funded labs are racing to build something extraordinary. Most of them plan to keep it behind closed doors. I get why. There's money involved, there's power involved, and there are real safety concerns. But there's another version of this story where the tools, the models, the research, all of it, gets shared openly. I find myself drawn to that second version, even though it's messier and harder and comes with its own set of problems.

I'm not an expert. I don't work in AI. I'm just a person who reads too much about this stuff and has opinions about it. I started this blog because I wanted a place to think through these questions out loud. Sometimes I'll write about something I read that got me thinking. Sometimes I'll just ramble about an idea that wouldn't leave me alone during a long walk.

No schedule, no promises. Just thoughts.