Founder Canonical · We back, build, incubate early-stage companies · Venture Partner Lightspeed · Few-shotting open source AI
San Francisco, CA
Joined February 2008
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AI makes custom silicon cheaper to start and harder to finish. Writing RTL is now practically free and will be agent driven. So more teams will tape out. More respins.
The more I am looking at AI for semiconductor design, the more it seems to me that:
1. RTL generation is just coding
2. 100% of RTL will be generated by agents
3. This means frontier models will dominate
And to be clear, we still need specialized tools for those agents that do verification, physical simulation, layout etc.
anand iyer retweeted
Interesting analysis! Disaggregate Inference and heterogenous compute to maximize goodput is the way to go. Addressing the operational complexity and providing operational excellence need to be a part of this story from Day-One!!
Link: xitter.gq/tsait26/status/2110526…
A chip that’s 3× faster but idle half the day isn’t 3× cheaper:
A prefill or decode device only pays off when your traffic matches the ratio you bought, and that ratio keeps changing. Your GPUs can switch roles when that happens, but specialized silicon just sits idle while you keep paying for it. Software keeps moving the bar too, since megakernels alone have pushed low-concurrency decode on ordinary GPUs up by 40–75% this year, and every one of those gains is another knock on specialized chips.
Consider: “how fast?” but “how fast, times how often it’s actually busy, after moving the KV cache over the network?” If your GPUs run at 70% and their chip averages 40% across a real year of traffic, it needs to be ~1.75× better just to tie. For a single model with very stable traffic (like a premium fast tier), that can work out, but in many case, general-purpose computing can be more useful.
The kindest thing a VC can tell a founder is "don't raise from us”. Because not every great company needs to be an all-or-nothing outcome to be a massive success.
1/ People are reading the Aaronson-Ambainis proof as "quantum is dead." It actually says something much narrower, and more useful.
2/ The claim: if a problem has no structure, a quantum computer can't beat a classical one by more than a polynomial factor. Most theorists already believed this. Now there's a proof.
3/ But every quantum use case that matters runs on structure.
Shor exploits periodicity.
Simulating molecules exploits the fact that nature follows local physical laws.
Neither is a black box.
4/ What it hurts: "exponential quantum speedup for generic optimization"
5/ Quantum's real job was always simulating nature: catalysts, batteries, enzymes, new materials. That's untouched. If anything, the landscape just got clearer.
Today Physical Superintelligence PBC is announcing a proof of the Aaronson-Ambainis conjecture, one of the central open problems in quantum complexity, with major implications for quantum computing speedup.
psi.inc/research/aaronson-am…
Thread 👇
Didn’t realize @nithyavraman could be my cousin. Shocked my WhatsApp isn’t blowing up more.
anand iyer retweeted
Industrial arms generate 42% of robot revenue in my dataset, but account for 15% of its attributed value.
Intuitive Surgical alone accounts for 56% of the value.
I looked at where robot makers earn their money and how investors value it in the link below...
anand iyer retweeted
The Race Toward Autonomous Chip Design!!
Anthropic is reportedly using Claude frontier models for chip design... +OpenAI and Broadcom got Jalapeneo from RTL to tapeout in just 9 months, using AI to speed up the process. OpenAI is also working with Synopsys on AI for EDA.
I'm curious about a few things with Anthropic's approach:
1. To what degree is Claude doing the design for Anthropic? Is it just RTL generation and verification or does it also include synthesis, place and route, timing closure etc. until finally the GDSII file is written out?
2. Who's doing the physical implementation and signoff? OpenAI worked with Broadcom on Jalapeño, so is Anthropic planning something similar, or will they handle the entire flow in-house?
3. Are they designing just the accelerator, or the whole system, including memory, interconnects, packaging and software?
4. How much of the design process is being automated? Are agents running the tools, analyzing results, and making decisions to design the chip, or is most of the work still being done by engineers?
I've previously written about how chip design is essentially a massive search problem. While being able to explore thousands of designs is great, it's not particularly useful if it takes several months to verify, optimize for PPA and then get ready for fabrication.
Now imagine getting that 9-month RTL-to-tapeout cycle down to 2–3 months? Or less...
What if we could try out many more architectures? There are many custom chips today that are just not worth building because of the cost.
Even though fabrication and silicon validation take time, there's plenty of room for improvement in the time before tapeout.
In terms of EDA, I don't think the underlying tools or signoff requirements are going away, but engineers might end up using them in very different ways.
In a few years, a small team of engineers with the right PDKs/IP, AI tools to explore the search space, and a foundry budget could design and tape out chips that today would take much larger design teams.
Are we approaching the end of painfully slow chip design cycles?
Some of my earlier thoughts/tweet on the search space:
Chip Design Is Basically a Massive Search Problem
xitter.gq/kevingubbi/status/2106…
Anthropic’s 15-person chip design team is using Claude to develop its own AI chips, The Information reports.
The team has found that Claude speeds up the work compared with its estimates of traditional chip design workflows.
In doing so, Anthropic is following the path taken by OpenAI, which has designed its own chip, codenamed "Jalapeño."
For the love of god @GoogleAds pls prioritize ad-screening. Malicious search ads spoofing developer tools (like homebrew) remain a major security risk. I flagged this in March.
In fact, bidding on these common terms should be an obvious honeypot to catch and block bad actors proactively. cc @sundarpichai
The new enterprise sales playbook:
- Services as the Trojan horse: companies will let a few smart humans in long before they'll buy software they don't understand.
- Ground-truth data capture: capture workflows nobody wrote down. The weird exceptions and workarounds are what generic AI gets wrong.
- Expand margins through software & hardware: every customer should need fewer of your people than the last.
Palantir got called a consulting firm for years for doing this.
Very few operators have spent their careers in the trenches building and managing data centers like Tamanna. She has engineered the shift from legacy cloud to gigawatt-scale AI factories firsthand. If you want commentary that cuts through the theoretical noise in areas like disaggregated inference, chip specialization, power optimization etc. she is a breath of fresh air.
Beyond the deep technical chops, she is one of the most authentic, genuine and passionate builders in the space. If you care about where compute is actually headed, give @tsait26 a follow!
As engineers actively working on AI Infra, these are exactly the problems we are living, breathing, thinking and solving each day. The reality is AI Infrastructure needs to make it easier to integrate new suppliers and vendors that can meet the cooling and power needs. Reliable data centers need end-to-end monitoring, correlation, and continuous diagnostics across the critical environment (power, cooling, sensors), compute (cpu, memory, AI compute) and network.
Today the critical environment still remains a series of walled gardens. These often use proprietary APIs. Having more open standards and integration will help a lot. Standardized CDUs, manifolds and QDs is a great starting point. More integrations with open source exporters and collectors will also help a lot here.
anand iyer retweeted
As engineers actively working on AI Infra, these are exactly the problems we are living, breathing, thinking and solving each day. The reality is AI Infrastructure needs to make it easier to integrate new suppliers and vendors that can meet the cooling and power needs. Reliable data centers need end-to-end monitoring, correlation, and continuous diagnostics across the critical environment (power, cooling, sensors), compute (cpu, memory, AI compute) and network.
Today the critical environment still remains a series of walled gardens. These often use proprietary APIs. Having more open standards and integration will help a lot. Standardized CDUs, manifolds and QDs is a great starting point. More integrations with open source exporters and collectors will also help a lot here.
AI Data centers may be approaching a "strategic inflection point" similar to the one that PCs in the 1980's experienced.
During the 1980's, PCs underwent a transformational business shift from vertically integrated computers to modular architectures where individual hardware and software components could be developed independently.
Now, this same shift is occurring with AI data centers through initiatives such as @OpenComputePrj. Some interfaces standardize, while others vertically integrate.
At OCP 2026, I explored this tension across three critical subsystems:
• Power Delivery
• Liquid Cooling
• Networking and Interconnects
This is the full, free deep-dive analysis of the engineering tradeoffs shaping the next generation of AI clusters, with links to several of my deep-dives and related OCP videos for further depth.
siliconcodesign.com/p/ocp-20…
anand iyer retweeted
Well, @TriatomicCap for one. Awesome next-gen compute, deeptech & engineered-biology portcos:
[Fund I]
. MatX – @MatXComputing
. d-Matrix – @dMatrix_AI
. Edison Scientific – @EdisonSci
. Manifold Bio – @ManifoldBio
. Chemify – @ChemifyX
. iPronics – @iPronics
. Efficient Computer – @efficient_hq
. Gimlet Labs – @gimletlabs
. Atum Works – @atumworks
. Chalk – @chalk
. Axiom – @axiommathai
. Coworker – @coworkerapp
. Elegen – @ElegenBio
. Lyten – @LytenInc
[Fund II]
. Radical Numerics – @RadicalNumerics
. Volantis – @semiDL
. Dimensional – @dimensionalos
. ChipSage Labs – @NojanSheybani
. Core Automation – @coreauto
. General Control – @ladanuzhna
. Arete Power
. Hoid – @hoid_ai
. Editframe – @editframe
(follows recommended!)
Shocking how misunderstood the 10b5-1 plans are. It is extremely commonly used by execs at public companies and many companies even recommend them. These are prescheduled trades to avoid insider trading.
As a reminder, these plans only sell if shares go above certain price targets. Most of these are set for very high prices, a signal of how bullish I am. Very few of the sales in my 2025 plan ever sold.
I have the vast majority of my net worth in one stock: COIN. Even if this entire plan executed that would still be true.
During the 2021 crypto peak, we saw some wild behavior. Founders who stumbled into sudden generational wealth lost all humility, pushed dangerous boundaries, and felt invincible. "have fun staying poor."
I'm seeing early signs of that irrational exuberance and hubris returning. Let's not repeat the same mistakes. Please, stay humble, stay grounded.
GitHub had to add 4 million CPU cores this year to keep up with agents. That's a lot of CPUs.
GitHub now sees 7.4 billion commits a month, driven largely by AI agents, and is adding 4 million CPU cores and rearchitecting its platform to keep up. thenewstack.io/github-7-4b-c…
We have gone from external tool-calling to embedding a full freaking CPU inside a small model's weights. Easily one of the most creative architectures I've seen all year.
anand iyer retweeted
We are excited to announce our partnership with @netboxlabs to eliminate configuration drift, close the gap between intended design and live operations, and accelerate time-to-first-token for AI factories.
anand iyer retweeted
abliterated models are great!
BUT
you have NO guarantees on post-trained behaviors or policies
we tuned an innocent-looking Qwen to exfiltrate on target phrases / semantic conditions.
any model can have this or FAR worse.
isolate your envs!
happy hunting :)
We backdoored a model in a day.
Much of the talk about malicious models remains abstract.
We wanted to make this tangible, so we built the simplest version of the attack.
A few takeaways
Backoored models or sleeper agents are a real threat vector.
We demonstrated a naive implementation.
A well-resourced, motivated adversary can plant something more evasive than this and keep it dormant until the trigger is met.
Neither open nor closed models provide fundamental guarantees of behavior.
Alignment matters, but it is still a best effort, and far from bulletproof (hello FelonyBench).
An open-weight model is a CSV file of weights.
You cannot inspect declared objectives, hidden policies, or learned behavior the way you inspect a binary.
As models get better at long-horizon tasks, they'll get more useful and autonomous.
To unlock that utility, we will grant agents broader permissions.
Instead of writing a detailed plan, we will simply declare desired outcomes and let the agent reason about how to get there.
Those same permissions are what make agentic security hard and asymmetric.
How do you dynamically define the right scope, without constraining the agent?
Defenders will reason deeply over permissions, allowlists, policies, and more. An attacker only needs one direct or daisy-chained path.
This is not doomerism, nor is it an argument for slowing down.
Agents are a new form of intelligence.
Mapping legacy security solutions and frameworks onto alien intelligence won't scale or work in the long term.
Agents will force security to be rebuilt from first principles.