AI Prompt:
I'm sort of skipping around lately. I used to limit my "big" questions for you and use another chatbot for relatively "small" questions, but your analysis seems to still suit my style. So, forgive me for asking a pretty mundane question and humor me. The question, of course, will be seen by many as an investment question, but I seriously / honestly don't use it for that purpose. I am truly fascinated by how the fourth industrial revolution / AI is playing out. The software continues to get a lot of headlines -- Anthropic, OpenAI, Gemini -- but what fascinates me most is the hardware story. We used to say that, in the "fab" world, all roads lead to TSMC. That remains true. But among the designers, it seems all of a sudden, all roads lead to Broadcom. The recent partnerships involving Broadcom have been amazing. Google and Apple (Baltra) come to mind. Two questions: among the large designers, does Broadcom now lead the pack; and, if so, why? The second question, over the horizon do you see any smaller, independent designers that might be making headlines in the 2030s? It seems a lot of these companies are out of Israel and completely unknown to the rest of us.
AI reply:
You’re not skipping around—you’re actually zeroing in on one of the most structurally important shifts in the AI stack right now. The “software gets headlines, hardware captures economics” dynamic has repeated in every computing cycle, and we’re watching it happen again in real time.
Let’s take your two questions directly.
1) Does
Broadcom Inc.
now lead the pack among chip designers?
Short answer:
Not across all chip design—but in one very specific and increasingly dominant lane, yes: custom AI silicon + networking for hyperscalers.
To understand why, you have to separate the chip world into three lanes:
A. Merchant silicon (one-to-many)
These design chips that they sell broadly.
👉 This is still NVIDIA’s world. No real debate there.
B. Captive silicon (in-house hyperscaler chips)
👉 These companies design chips for their own use.
C.
Custom silicon enabler (this is Broadcom’s lane)
This is where Broadcom Inc. has quietly become the central player.
They:
-
Co-design chips with hyperscalers
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Provide IP blocks (networking, SerDes, switching)
-
Handle system-level integration
-
Leverage TSMC for fabrication
👉 Think of Broadcom as:
“The outsourced chip design arm for companies that don’t want to build a full NVIDIA internally.”
Why Broadcom is winning
now
1. The bottleneck shifted from compute →
interconnect + scale
Training large models is no longer just about FLOPs.
It’s about:
-
Moving data between chips
-
Synchronizing thousands of accelerators
-
Power efficiency at rack and cluster scale
Broadcom dominates:
-
Ethernet switching
-
Custom interconnect ASICs
-
High-speed I/O (SerDes)
👉 This is exactly where AI clusters choke.
2. Hyperscalers want independence from NVIDIA
-
NVIDIA’s stack is powerful—but vertically integrated
-
Margins are enormous
-
Roadmap control sits with NVIDIA
So companies like Google and Apple ask:
“Can we build something 80–90% as good, but fully under our control?”
Broadcom is the answer.
3. Broadcom is “Switzerland”
Unlike:
-
NVIDIA (competes with customers)
-
Intel (historically conflicted)
Broadcom:
-
Doesn’t run cloud
-
Doesn’t compete with hyperscalers
-
Doesn’t try to own the full stack
👉 That neutrality is incredibly valuable.
4. They operate at system-level, not chip-level
Broadcom isn’t just designing chips—they’re helping design:
-
Entire AI clusters
-
Network fabrics
-
Data center architectures
That’s a higher layer of control than traditional chip design.
So—do they “lead”?
-
AI compute chips: No → still NVIDIA
-
CPU / general compute: No → fragmented (AMD, Apple, etc.)
-
Custom AI + networking infrastructure:
👉 Yes, arguably the central orchestrator
That’s why it feels like “all roads lead to Broadcom.” In their lane, they’re becoming indispensable.
2) Who are the “unknown” designers that could matter in the 2030s?
You’re exactly right about Israel—that ecosystem is disproportionately important.
Rather than give a random list, it’s more useful to group them by where the next bottlenecks are:
A. Interconnect / optical / data movement (VERY important)
AI scaling is increasingly about moving bits, not computing them.
Watch:
-
Marvell Technology (already public, but underappreciated)
-
Mellanox Technologies legacy influence (Israel-based talent still critical)
Emerging (less known):
-
Optical interconnect startups (many in Israel & Silicon Valley)
-
Co-packaged optics players
👉 This area could produce “the next Broadcom-like winner.”
B. AI-specific architectures (post-GPU era bets)
Cerebras Systems
-
Wafer-scale chips
-
Radical approach (not incremental)
Graphcore
-
IPU architecture (struggled commercially, but ideas matter)
SambaNova Systems
-
Dataflow architectures
👉 Most will fail—but one or two ideas could define the next paradigm.
C. Chiplet + modular design ecosystem
The future may not be monolithic chips.
Look for:
-
Chiplet interconnect standards
-
Advanced packaging innovators
(Today this is led by giants, but startups will emerge around tooling and IP.)
D. Edge AI specialists (quiet but powerful)
Israel is particularly strong here:
-
Low-power inference chips
-
Vision processors
-
Defense-derived AI hardware
Examples:
👉 If inference explodes (as you were asking earlier), this category becomes huge.
E. RISC-V ecosystem players
An open alternative to ARM:
-
Custom AI accelerators built on RISC-V
-
Sovereign chip efforts (Europe, India, Middle East)
Still early—but strategically important.
The deeper pattern (this is the real answer)
What you’re noticing with Broadcom is not accidental. It reflects a shift:
Then (2010s):
-
Compute was scarce → GPUs dominated
Now (mid-2020s):
-
Compute + networking + system design all matter
-
Hyperscalers want vertical control
Next (2030s):
-
Bottlenecks likely shift again:
-
Memory (HBM alternatives?)
-
Power
-
Interconnect (optical?)
-
Physical scaling limits
-
👉 The winners will emerge where the constraint is tightest.
Bottom line
-
Broadcom Inc. isn’t “the new NVIDIA”
-
It’s something arguably more subtle—and durable:
The arms dealer + architect of hyperscaler AI infrastructure
And those positions tend to compound quietly for a long time.
If you want to push this further, the most interesting next question (and one you were already circling earlier) is:
What happens when inference dominates over training?
Because that shift could reshuffle this entire leaderboard—including Broadcom.
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Second Reply
I asked another chatbot the same question and got this response:
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