A slow down in client / consumer demand for the market will provide infinite wafer availability to $AMD via TSMC.
Roughly 50-60% of TSMC’s 2 and 3 nm wafers are directed towards client and consumer electronics.
AMD will have any amount of wafers they need for server CPUs.
Reduced demand for client products will also free up memory capacity for AI servers.
I missed the point where $AMD lost market share here? Where AMD revenues fell?
$INTC sucks because they suck.
Did demand suddenly go down? No, demand is way up. You can’t get memory because demand is so freaking high.
Intel is crashing because they suck at making chips. You know who doesn’t suck at making chips, TSMC.
Intel’s incompetence is an opportunity for AMD. The more those fucktards bumble fuck around for another year, the more opportunities AMD will have to fill the demand they can’t execute on.
Aletheia is the best research house. Their industry intelligence is truly impressive.
Aletheia: Anthropic has increased its AMD GPU orders for 2028 to 3 GW—three times its original deployment target of 1 GW.
Aletheia: OpenAI has increased its total planned AMD GPU deployments over 2026–2028 from 6 GW to 8–9 GW.
I don’t know when $AMD is going to double, but if I had to guess, it would be sooner rather than later.
Not financial advice.
Perhaps the bigger fraud on this spec sheet is 3.6 exaflops.
No one uses sparce compute numbers on their spec sheet.
It is truly the act of a charlatan to try and pull the wool over the eyes of the smartest people on earth with the dumbest scam.
xitter.gq/0xmitsurii/status/2001…
$AMD Helios now clears Rubin NVL72 by a wide margin.
AMD trades at $0.854T while Nvidia trades at $5.18T.
It is fast becoming time to ask whether or not AMD will sell more AI racks than Nvidia in the not too distant future.
Helios provides vastly more HBM capacity, meaning more model instances over the same number of racks.
Now they will provide more throughput by a substantial margin as Nvidia continues to nerf their racks.
Why would hyperscalers continue to spend more on Nvidia to get less?
This is how we end up with Taalas based fixed weight models brought to you by $AMD.
Frontier models are now too powerful for general release. There are no new models coming folks.
We might spend the next few months making the existing frontier models more efficient to inference or higher RL capabilities, but more powerful is done.
Next we take the best models and etch them into silicon with Taalas based chips. This will improve model inference efficiency 50x.
Helios can handle prefill and Taalas can handle decode. This will smoke anything on the market and send AMD into the stratosphere.
two weeks ago Sam Altman defended open source AI..
now he says his best model is too dangerous to release..
then..
society will have to accept severe cyber incidents..
as the price of open source liberty..
now..
OpenAI can't responsibly open source its best internal model..
not until cyber defense and bio safety catch up..
to be fair..
he's backing open source in general..
just not at the very frontier..
but then he admits the scariest part..
the gap to an open model just as powerful..
is "not super" big..
this same week..
hackers used AI agents to attack hundreds of organizations..
the closed labs think they're buying the world time..
the real question is how much time is left
One thing not accounted for with Anthropic is their access to Superhuman Intelligence.
They could do something like double the efficiency of solar cells with new materials and get a 10% cut of every solar cell produced worldwide.
We won’t see that AI, it is too powerful for public release, but it doesn’t stop them from licensing who knows how many breakthroughs.
The interesting dynamic is compute restrictions have always been based on Nvidia’s poor yields on reticle sized monolithic compute dies.
Yields on that crap have to be like 50-60%.
$AMD doesn’t have this problem, the compute is split into 8 chiplets. Yield is north of 80%
The interesting thing is $AMD divides its compute into chiplets. A GPU
If you run a business predicated on people doing work at a computer, you are not concerned about spending too much on AI.
Your principal concern is that your competitors will figure out before you and make you irrelevant.
$AMD
It just so happens that the whole AI trade is suddenly “over” on options expiration day for October in the quiet period before earnings.
Wall street thinks you are stupid.
I am buying every share I can get.
$AMD
The signs were everywhere but no one understood them.
SHOCKING: We have confirmed that multiple Neoclouds have been refused access to GTC Berlin due to them adding AMD GPUs, TPUs, and other chips to their cloud offerings
Note that this doesn't apply to hyperscalers, as they have more buying power. But for neoclouds, executives feel NVIDIA has been using high-pressure tactics to keep them NVIDIA-only.
As such, neocloud executives are starting to consider offering TPUs or AMD GPUs quietly to avoid getting pressured by NVIDIA and to avoid the feeling that NVIDIA will retaliate.
xitter.gq/SemiAnalysis_/status/2…
This is the most important chart in AI. It will take a generation for the realization to set in, so MI455X will be a proof of economics at scale.
There is trepidation to go all in by hyperscalers for $AMD because they are used to just buying Nvidia and the customers run Nvidia.
Having Meta, Open AI and Anthropic bought in is huge but, the delta for AMD ecosystem isn’t understood yet.
By next year, the scale of the benefits will be fully understood, exactly in time for mi555x to expand the TCO proposition.
I feel like $GOOG has the inside track to most corporate AI revenue by far.
They have all the customers in their cloud business for decades.
These customers don’t have to do anything but have Google apply AI to their work flows.
Google makes frontier level models that they can serve cheaper because they have TPU.
Open AI is raising funding at $1.5T while $GOOG is trading at $4T.
Google is undervalued by at least 50% currently.
