It depends on how quickly you can bake new architectures.

Text diffusion might be a disruptor here, but let me just say the most cutting edhe form of image diffusion (JiT and DiT) right now is just a big fat stack of alternating attention and MLP matmulls. Not theoretically hard to bake

If you’re only running models for frontier capabilities, yeah. For tasks where current models are smart enough, running them 100x faster is the most impactful improvement you can make. Consider all the things you could use a model for, but don’t, because the latency is just a bit too high.

I'd gladly pay for a Claude Opus 4.6 Thinking High in silicon and use it for 1-2 years. It's good enough for many coding tasks.

Depends on how much it costs the consumer. If I could buy a "cartridge" of Kimi K3 for 300 bucks I 100% would buy that shit asap. Even if it's "no good" after lets say 4 months still would be worth it IMO.

"seems like baking models into silicon is speed-running obsolescence"

Now maybe. When models are flying passenger aircraft, other prerogatives will assert themselves. When a 50TB ROM means you can impulse purchase a ChatGPT 6.3 xhigh that runs on batteries, yet more use cases will be apparent.

I could see this making sense when model development start to settle down ... it's going to settle down, right? ...

Not sure. You can fix the transistors but leave the connections between them open for flexibility, so you only need to change the manufacturing process for the upper masks for every new model.

Compute the cost of producing n of them devices, imagine a fair price based on that, and see if that local, blazing fast card* can be an asset that could be replaced periodically.

*(It's local: private files managing firm oriented. It's blazing fast: it can be placed into recursive, intensive local workflows.)

Which is exactly what companies and shareholders want to increase sales.

Look at it the other way: compared to the cost of training a model, the cost of making a custom ASIC is trivial.

obsolescence is the whole point. apple gets to sell a new phone very 6-12 months because of it.

i have written about this:

"For device makers

Packaging models with laptops and smartphones will let application access near free, low latency inference and potentially offer users a better experience with the option of preserving data on-device. This is viable under the condition that tasks that do require larger expert models that run in the cloud can be routed to external models. A side-effect of local models and what will let Apple cut upgrade cycles from ~4 years (?) down to 12-18 months is specialized hardware to run them. For almost a decade, smartphones have been trying to compete on better cameras. This coming decade will see them selling better GPUs, NPUs, ASICs and whatever other things they'll be calling the inference chips, to drive re-purchase. Every six months will see a better model on new hardware, which will enable better performance in certain applications."

https://try.works/role-model-the-case-for-a-model-routing-pr...