nimakeivan

Karma: 27

I co-founded durable.ai, a platform for synthesizing and maintaining long-running software from requirements drawn up by non-technical enterprise users.

Before Durable, I co-founded Canvas Technology, a Boulder, Colorado startup developing vision-based mobile autonomy for manufacturing and logistics. Canvas was acquired by Amazon in 2019.

Post-acquisition, I was a venture partner at Xplorer Capital, helping the team source and assess early-stage deals in deeptech, AI, and sustainability.

I hold a PhD from the University of Colorado Boulder, where my research focused on visual perception and control for agile autonomous vehicles.

You can read my writing, browse my talks and research, or get in touch.

nimakeivan 14 Aug 2026 3:39 UTC
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in reply to: Vladimir_Nesov’s comment on: Unblocking AI’s Continual Learning: Hints From How Humans Learn
So there is no technical reason for there being an economic benefit from knowing less
I think you’re still assuming there’s this one megacompany that’s training a single model distributed to the whole world. A small model can be trained by a specialized company that’s a good enough bootstrap based for a basket of skills such that CL can then specialize it for a user environment. If that small model can take market share for applications that require that basket of skills away from the mega AI corp, then you have the PC/mainframe dynamic again where the mainframe play gets completely disrupted.

nimakeivan 14 Aug 2026 3:36 UTC
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in reply to: AnthonyC’s comment on: Unblocking AI’s Continual Learning: Hints From How Humans Learn
A mainframe is a single large very powerful machine. A datacenter is a bunch of servers (the PC model) that are conveniently colocated. I think the analogy is consistent with data centers.
GPU datacenters are different and closer to one giant machine, but that’s a recent phenomenon with centralized training of AI.

nimakeivan 13 Aug 2026 23:01 UTC
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in reply to: Vaughn Papenhausen’s comment on: Unblocking AI’s Continual Learning: Hints From How Humans Learn
Without CL, the main axis the model companies compete on is larger, more broadly capable models. The point I made in the other threads is that if you’re a SMB and need a slack AI to manage customer service, you don’t need that AI to also know organic chemistry. That it does today is an economic abberation simmilar to the adoption of mainframes before the PC came around. The PC decimated mainframes not because it was more powerful, but because it was cheaper, right-sized, and personalized.
If it’s true that CL is the missing capability for businesses to get ROI on AI, and not marginal improvement on group theory, or nuclear engineering, or biology, then its introduction will dampend incentives to ship ever larger models with “dangerous” knowledge very few businesses actually need, even if those larger models would similarly benefit from CL.

nimakeivan 13 Aug 2026 21:16 UTC
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in reply to: Vladimir_Nesov’s comment on: Unblocking AI’s Continual Learning: Hints From How Humans Learn
You can’t just apply the improved method to the smaller models, and compare that to the bigger models without the improved method
I tried to address this but might have been missed:
It’s not clear to me that large models getting better with CL means the adoption (not capability, as measured by centralized benchmarks) gap between them and small models will hold. First, small models will get more benefit than large models. But more importantly, if the extra capabilities of the larger model are not economically meaningful against the costs, then the market will select the smaller model.
So both get a benefit, but if what was blocking economic ROI was CL, and not the extra capabilities of the larger models, then smaller models will win.
They still aren’t capable enough at whatever they aren’t in fact capable enough for. And those further things can have enormous TAM,
Yes, and I think we’ve established the “further thing” is CL (at least in the context of this argument), so then the enourmous TAM is tied to CL, not how big the model is. You would obviously want to own the weights, and you’d want to serve the smallest possible model that delivered the returns of the CL to your org.
If the gap remains, while the smaller models are capable enough for most general economic tasks, then the bigger models will be even more capable than that.
That extra capability only matters if it’s economically useful. Like I said before (and analogized with the mainframe vs. PC argument), the model being able to solve math conjectures is not useful for the white collar work that has repeatedly resisted automation with AI. More of that will not change the equilibrium.

nimakeivan 13 Aug 2026 20:22 UTC
2 points
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in reply to: Vladimir_Nesov’s comment on: Unblocking AI’s Continual Learning: Hints From How Humans Learn
Why is that? Bigger models are better at making use of any given amount of data, so they might benefit more, if there’s enough compute to go around.
The argument assumes that frontier models are already capable enough (“PhD level”) for most general whilte collar economic tasks that they’re being applied to, and that what’s missing is the tacit knowledge enabled by CL. Making bigger models bigger just won’t fix this blocker.
In some ways, the modern 1 GW datacenters beg to differ.
Yes, and to use the analogy, it’s because we’re in the mainframe era. Once CL is available, it would be akin to the introduction of the PC.

nimakeivan 13 Aug 2026 19:59 UTC
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in reply to: Vladimir_Nesov’s comment on: Unblocking AI’s Continual Learning: Hints From How Humans Learn
Arguments/ideas about what would make smaller models catch up to the frontier should confront what happens when you use the same improved methods for the biggest models. Usually, it doesn’t narrow the gap, because both things get stronger.
It’s not clear to me that large models getting better with CL means the adoption (not capability, as measured by centralized benchmarks) gap between them and small models will hold. First, small models will get more benefit than large models. But more importantly, if the extra capabilities of the larger model are not economically meaningful against the costs, then the market will select the smaller model. A historical analog is the application of Moore’s law to both mainframes and PCs. Both got better, but the market selected the lower-cost, right-sized, and personalized option.

nimakeivan 13 Aug 2026 19:50 UTC
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in reply to: Vladimir_Nesov’s comment on: Unblocking AI’s Continual Learning: Hints From How Humans Learn
How would they do that?
Because the marginal benefit from a smaller model learning your company’s tacit knowledge far outweights the marginal benefit from training a model with larger parameters. Like your marketing agent just doesn’t benefit from GPT6 solving yet another math conjecture. It does benefit from knowing your business and your customers and the subtleties of your particular industry.
Not without destroying the current prospects for AI company growth.
Yes, and I don’t think AI company growth prospects are a universal given. If we do end up with CL models, companies likely won’t want the learned knowledge to be distributed with other users of the model. So the incentive will be to own the weights, which directly threatens the business of the frontier model companies and their capacity to centrally train ever-larger models.

nimakeivan 12 Aug 2026 22:51 UTC
3 points
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in reply to: Seth Herd’s comment on: Unblocking AI’s Continual Learning: Hints From How Humans Learn
Thank you for the comment, Seth. I’m very open to a discussion and a belief update. The reason I am interested and in and optimistic about continual learning is because I think it allows smaller, more personalized models to be successful. A few reasons that I touched on briefly in the essay:
- Large (and getting larger) models are efficient to deploy in part because their weights are frozen. Deploying continual learning is small-model territory. Even as compute and memory availability expand, the delta in deployment difficulty between frozen and continually learning weights will remain.
- Large, centrally trained models not only suffer from mode collapse themselves, they mode-collapse the humans that use them. If we use models that all know the same things and think the same way, what happens to the pluraly of human thought and culture?
- Broader economically productive deployment of smaller and more personalized models (downstream of continual learning) will disincentivize training ever larger models. Especially if limits (as I’ve argued) to ROI are no longer on raw knowledge/intelligence/agency but rather missing tacit knowledge.

nimakeivan 12 Aug 2026 18:38 UTC
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on: Unblocking AI’s Continual Learning: Hints From How Humans Learn
I’d very much appreciate a discussion on the argument.

Some ways I could see pushing back in it:

Humans learn better out of order: there are studies showing this, but as far as I could find the material being shuffled were not compositionally depenedent, meaning they could be learned in any order.

Better architecture, not priors: in a previous article on AlphaFold, I argued that its hard-coded inductive priors lent it the sample-efficiency it needed to learn something generalizable for the relatively sparse protein database. There, the priors were baked in a specialized architecture, but humans are sample-efficient more broadly. So perhaps there is an architecture that is just more sample efficient without needing ordered training or hierarchical priors.

What about replay: our brain does replay experiences, which nominally mitigates catastrophic interference. Although the replay seems to prioritize surprise/novelty rather than already-learned experiences.

nimakeivan 2 Jul 2026 16:30 UTC
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in reply to: ChristianKl’s comment on: Why aren’t there more AlphaFolds?
I found the ACX post on the Midjourney Scanner helpful. My takeaway was that there are limitations to resolution and coverage of ultrasound tomography (for example, ultrasound can’t penetrate bone and struggles with cranial imaging). With large scale and their expertise, Midjourney may be able to squeeze more out of the data. I agree that it’s AlphaFold-like: the data is observational and any model needs to learn or approximate the mechanisms behind how it’s generated.