ChatGPT Deep Research dug through Taalas' WIPO patent filings and public reporting to piece together a hypothesis. Next Platform notes at least 14 patents filed [1]. The two most relevant:
"Large Parameter Set Computation Accelerator Using Memory with Parameter Encoding" [2]
"Mask Programmable ROM Using Shared Connections" [3]
The "single transistor multiply" could be multiplication by routing, not arithmetic. Patent [2] describes an accelerator where, if weights are 4-bit (16 possible values), you pre-compute all 16 products (input x each possible value) with a shared multiplier bank, then use a hardwired mesh to route the correct result to each weight's location. The abstract says it directly: multiplier circuits produce a set of outputs, readable cells store addresses associated with parameter values, and a selection circuit picks the right output. The per-weight "readable cell" would then just be an access transistor that passes through the right pre-computed product. If that reading is correct, it's consistent with the CEO telling EE Times compute is "fully digital" [4], and explains why 4-bit matters so much: 16 multipliers to broadcast is tractable, 256 (8-bit) is not.
The same patent reportedly describes the connectivity mesh as configurable via top metal masks, referred to as "saving the model in the mask ROM of the system." If so, the base die is identical across models, with only top metal layers changing to encode weights-as-connectivity and dataflow schedule.
Patent [3] covers high-density multibit mask ROM using shared drain and gate connections with mask-programmable vias, possibly how they hit the density for 8B parameters on one 815mm2 die.
If roughly right, some testable predictions: performance very sensitive to quantization bitwidth; near-zero external memory bandwidth dependence; fine-tuning limited to what fits in the SRAM sidecar.
Caveat: the specific implementation details beyond the abstracts are based on Deep Research's analysis of the full patent texts, not my own reading, so could be off. But the abstracts and public descriptions line up well.
> While the LLMs get to blast through all the fun, easy work at lightning speed, we are then left with all the thankless tasks: testing to ensure existing functionality isn’t broken, clearing out duplicated code, writing documentation, handling deployment and infrastructure, etc.
I’ve found LLMs just as useful for the "thankless" layers (e.g. tests, docs, deployment).
The real failure mode is letting AI flood the repo with half-baked abstractions without a playbook. It's helpful to have the model review the existing code and plan out the approach before writing any new code.
The leverage may be in using LLMs more systematically across the lifecycle, including the grunt work the author says remains human-only.
> I wondered how soon will I be able to ask the video player "What am I looking at here, describe the equations" and it will OCR the frames, analyze them and explain them to me.
"Large Parameter Set Computation Accelerator Using Memory with Parameter Encoding" [2]
"Mask Programmable ROM Using Shared Connections" [3]
The "single transistor multiply" could be multiplication by routing, not arithmetic. Patent [2] describes an accelerator where, if weights are 4-bit (16 possible values), you pre-compute all 16 products (input x each possible value) with a shared multiplier bank, then use a hardwired mesh to route the correct result to each weight's location. The abstract says it directly: multiplier circuits produce a set of outputs, readable cells store addresses associated with parameter values, and a selection circuit picks the right output. The per-weight "readable cell" would then just be an access transistor that passes through the right pre-computed product. If that reading is correct, it's consistent with the CEO telling EE Times compute is "fully digital" [4], and explains why 4-bit matters so much: 16 multipliers to broadcast is tractable, 256 (8-bit) is not.
The same patent reportedly describes the connectivity mesh as configurable via top metal masks, referred to as "saving the model in the mask ROM of the system." If so, the base die is identical across models, with only top metal layers changing to encode weights-as-connectivity and dataflow schedule.
Patent [3] covers high-density multibit mask ROM using shared drain and gate connections with mask-programmable vias, possibly how they hit the density for 8B parameters on one 815mm2 die.
If roughly right, some testable predictions: performance very sensitive to quantization bitwidth; near-zero external memory bandwidth dependence; fine-tuning limited to what fits in the SRAM sidecar.
Caveat: the specific implementation details beyond the abstracts are based on Deep Research's analysis of the full patent texts, not my own reading, so could be off. But the abstracts and public descriptions line up well.
[1] https://www.nextplatform.com/2026/02/19/taalas-etches-ai-mod...
[2] https://patents.google.com/patent/WO2025147771A1/en
[3] https://patents.google.com/patent/WO2025217724A1/en
[4] https://www.eetimes.com/taalas-specializes-to-extremes-for-e...