We got the confidential Series A deck. The wafer-scale bet, the climb to $510M, and what it says about the AI compute race.
In 2016, everyone was iterating on the GPU. One team looked at the same chip and named a problem the rest of the field worked around.
Only about 4% of a GPU’s silicon did the actual AI math. The rest was graphics machinery, carried along out of habit. Andrew Feldman’s read: the hard part of deep learning is moving data around, rather than doing the multiplication. So the right machine gets built around data movement, from a blank sheet.
That idea lived in a Series A deck marked working name, from a company so early its identity was still a working title and its vocabulary predated the word transformer. Most people who saw it thought it was crazy.
In May 2026, that company, Cerebras, IPO’d at a valuation near $56 billion, the biggest US tech IPO since Uber.
Here is the part worth keeping even if you stop here. Two questions decide a bet like this, and the deck answers both:
They saw AI compute going vertical while the broader industry still treated AI as a science project.Is the demand curve about to bend? The GPU was accidentally good at AI, a graphics chip pressed into service. That gap is the opening.Is the incumbent tool accidentally good, or deliberately good?
When demand is bending and the incumbent is only accidentally good, you have the setup for a generational company. That lens works on any hardware bet you evaluate.
Now the concrete part. We got the confidential Series A deck, and the numbers behind the climb. Below the line:
▫️
all 20 slides, with what they pitched versus what they shippedThe confidential Series A deck,▫️
the engineering slide that splits the headline claim into four honest multipliersThe 135x bridge,▫️
$25M to $510M, with the growth rates and the swing to profitThe revenue climb,▫️
from the seed off this deck to the $23B private mark to the $56B IPOEvery round,▫️ the pricing, the 68% pop, and the near-halving six weeks laterThe IPO play-by-play,▫️
the customer concentration and OpenAI dependency the headlines skipThe risk file,▫️
what this deck teaches anyone building in AI right nowThe operator lessons,
1 subscription unlocks every teardown
This is one breakdown in a growing library. Premium opens all of them:
▫️ [The AI tools and models library](https://www.the-ai-corner.com/t/ai-tools-and-models?r=1krivi)
▫️ [The business and investing library](https://www.the-ai-corner.com/t/business-and-investing?r=1krivi)
▫️ [The Claude and Anthropic library](https://www.the-ai-corner.com/t/claude-and-anthropic?r=1krivi)
Plus a fresh teardown every week. One deck like this, read before the room reads it, pays the subscription back many times over.
The full confidential deck, the slide-by-slide teardown, the revenue and funding history, the IPO play-by-play, and the risk file, in one place.
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