I don't see DeepSeek as having a negative impact on BRN's business, although it may take a while for the ASX to twig.
It is hard to get your head around the tech generally, let alone the nuances which differentiate the various players.
However, there are largish differentiators between Akida and DeepSeek, edge/cloud, software/silicon, training v inference ... .
That's not to say we can rest on our laurels (do laurels have thorns?) - there is allways the chance that a competitor could spring from the bushes. However, our continuous stream of technological advances will help to ware off that contingency.
We must avoid becoming predicatble ... all that stuff about eternal vigilance:
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Just on the training v inference dichotomy:
TRAINING:
It seems that DeepSeek compiles its models from other open source models.
Akida can also start with open source mdels and build on them. Akida does training really well with Edge Impulse.
INFERENCE:
DS uses software for inference.
Akida uses silicon for inference.
Now here's an analogy about use cases:
Think of the model as the library. In an initial phase, a lot of books are ordered and put on the shelves. This is equivalent to training.
As new books are added, this is equivalent to learning.
Then the people come to the library to obtain information by retrieving the relevant books and extracting the desired information. This can be thought of as inference.
This analogy is intended to show that the training phase of the model (stocking the shelves) is a large initial outlay. The adding of new books (learning new examples) can be an ongoing process, but the cost is small compare with training.
The main purpose of the library/model is to provide a resource for the users to carry out inference. Inference is the purpose of the model. Inferene is the purpose people use AI.
We know that Akida together with EI does training very well using N:M coding for optimal sparsity, as it does with learning (ML). Akida also does inference optimally using SNN, and N:M.
I don't know anything of how DS does inference other than it does it in software, and in the absence of any reference to "software secret sauce", I'll assune it's bog standard CNN, and we know that Akida eats software CNN.
So, in my view, the part of the SP fall today due to DS is misguided. If anything, this will boost Akida/TENNs because Akida/TENNs beats DS hands down at inference.
The caution on this is that, Akida does not do GenAI (analysis/reasoning), but for all the practical applications which do not require analysis/reasoning, Akida is superior.
That said, Akida can be used in conjunction with analysis/reasoning software and can relieve the software of much of the processing in cases like NLP, object tracking, etc., where long skip can play a part.