Ask any AI/ML expert and they will tell you that the key to a well performing model is a clean dataset. The rise of AI will financially incentivize human input, and thus independent human thinking. Frontier model reliance on RAG proves this point. Assuming AI will do all the thinking for you is a recipe for financial ruin.
Two, the cost to run an AI service != the cost to use an AI service. Frontier companies cannot operate at a deficit forever. Even if their costs go down, end user prices will go up. Their profit margins will increase and history shows they will not pass those savings to the end user.
> I hadn't actually explained what the software would do.
I cannot tell you how many READMEs I've read that follow the pattern: " is a written in ." I only have some semblance of what it does after using/seeing a demo; too often one that isnt available through the README.
The other variant is "Foo is an alternative to bar". While it could work in the niche you are in and it is totally fine to not target people outside of it, it could be really hard to get into.
Software moves so fast that its actually an anti feature. Libraries and SDKs measure lifetime support in years, but industrial hardware support *at minimum* is a decade. A lot of these new startups dont understand that established industry wont use them unless they known this shiny new system wont ve abandoned in a few years.
On the other hand, softwate devs trying to quickly make a product have something to get them going.
A lot of datasheets are inaccessible without signing an NDA. Even if you're a VC-backed enterprise, some unnamed chip vendors will tell you to pound sand if you're less than 8 figures of annual revenue.
Worse, many recommendations in datasheets were written in the 80s, overkill for many applications, or completely wrong and won't be updated until an errata is published 6 months later. Given current workflows, I don't see how AI would help designing a PCB with an alpha chip that's doing anything remotely novel.
More to the articles point, AI designing ASICs or other Verilog/HDL defined components could be very interesting use case
Not necessarily true, most recently I was working with an USB 3.1 chip that anyone can buy off DigiKey, but I had to call contacts at 4 seperate companies just to get firmware to make superspeed work. We werr just too small of a fish to make the chip vender care.
This is becoming more true as a larger percentage of hardware infrastructure is occupied by the datacenter economy.
Ask any AI/ML expert and they will tell you that the key to a well performing model is a clean dataset. The rise of AI will financially incentivize human input, and thus independent human thinking. Frontier model reliance on RAG proves this point. Assuming AI will do all the thinking for you is a recipe for financial ruin.
Two, the cost to run an AI service != the cost to use an AI service. Frontier companies cannot operate at a deficit forever. Even if their costs go down, end user prices will go up. Their profit margins will increase and history shows they will not pass those savings to the end user.
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