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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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.
anand iyer retweeted
The Jalapeño chip is pretty much a systolic array with a bunch of HBM. These designs are not that crazy, but it allows people to squeeze way more performance out of their existing models. This enables two new directions for startups:
1) build models that can modify your workloads to take advantage of every nook and cranny of the available hardware
2) build new chip design workflows that enable fast development and iteration on hardware
These should be happening in parallel so that workloads can be accelerated on available hardware while custom silicon is built. At the end of the day, faster chip design has to happen, because we won’t be on the transformer architecture forever, and workloads are just going to grow to be more and more complex as consumer expectations rise
Frontier labs making custom chips is not really as hard / as impressive as you might think.
1. They are primarily targeting inference not training. inference is easier
2. They are targeting transformers only. Think large MXU matmul units
3. They are writing all kernels in close to ISA with coding agents RLed for their ISA.
4. They aren’t making high level compilers for codegen
A frontier lab vending a true generic chip like a Nvidia GPU or Google TPU is MUCH harder than spinning up an in house inference program.
Still I think you will see many more inference chip companies as the cost of the software is just the cost of tokens.
anand iyer retweeted
Everyone is going to want their own chip. Generalized architectures like TPU and GPU are just not good enough for the performance these workloads are demanding
anand iyer retweeted
FUNDRAISING IS SUPPOSED TO BE THE EASY PART!!! Seeing way too many founders in SF especially viewing the fundraise as the end goal. As AI is essentially doing all critical thinking for people in this city, it’s never been more important to realize how CHEAP ideas are. The only thing that matters is execution and relentless forward motion. I’ve seen a good majority of founders just resting on their laurels as soon as the money lands in the bank. Job’s not finished!
anand iyer retweeted
Day 2 of CASPA summit sessions is over and it turns out chip design is still slow :O
My main takeaways from today’s panels below
The hardware side:
- Compute isn't the bottleneck anymore. Moving data is. Chip to chip interconnect can eat 60 to 70% of a package's energy
- Memory went from commodity to strategic. Every idle GPU waiting on data is burning money
- LOTS of new fabs dropping soon, particularly in Asia (78 out of the upcoming 102 fabs being built by 2029 are in Asia)
The chip design side:
- Models change in weeks, chips still take months. The goal is getting design from 18 to 24 months down to 9 (I think that’s not ambitious enough)
- THE BIG ONE: Agentic harnesses alone won't get us there. The engines themselves have to get faster (WE CANT KEEP LEANING ON LEGACY EDA)
- RL for chip design is stuck waiting on the tools. One run takes a day. People are begging for a couple of hours (YUP)
- Big debate on surrogate models. Some like them for exploring the design space with real sign-off after. Others think models learn better against the real tools, and a surrogate just gets reward hacked (but no ai for EDA companies will get access to the EDA tooling because that puts EDA companies at risk 😃)
anand iyer retweeted
PSA for macOS users:
1. Apple’s supplied SSL certificate bundle at /etc/ssl/cert.pem still trusts certificates attackers can forge using compromised eMudhra keys.
2. I confirmed this on macOS 26.7.1 and 27.0.1 using Apple’s built-in curl and Git.
3. An attacker intercepting connections from tools using this bundle could replace downloads with malware. With commands like curl … | sh, that malware would execute immediately.
4. Removing the roots that authorize those certificates blocks the tested chains. Both OS updates restored roots I’d previously removed, requiring me to remove them again.
4. I’ve submitted the findings to Apple’s security team and am awaiting their response. In the meantime, you can apply a temporary fix as outlined below.
5. To check and fix your machine, give an agent like ChatGPT/Codex or Claude Code the following prompt:
“Independently verify whether Apple’s /etc/ssl/cert.pem still permits trust in certificates signed with compromised eMudhra keys. If so, back it up and remove the roots enabling that trust, identified by verified SPKI fingerprints. Confirm Apple’s built-in curl and Git reject those chains while ordinary HTTPS still works. Preserve rollback and never disable TLS verification.”
Note that removing these roots can also block legitimate sites. This is a temporary mitigation, not a substitute for a fix from Apple.
I’ll share the script I used in a follow-up post if you’d prefer to use that directly.
PSA for Codex CLI users:
1. Codex 0.163.0’s release lockfile pins rustls-webpki 0.103.15 and root bundles that can still trust compromised eMudhra certificates.
2. An attacker intercepting your connection could impersonate a server, steal sensitive data, or alter responses, potentially tricking Codex into running malicious commands.
3. Codex 0.164.0, released October 23 at 12:48 a.m. ET, updates those dependencies to fixed versions.
4. To check your codex version run "codex --version". It should be at least 0.164.0.
5. To patch the vulnerability, run "codex update".
6. Apps that bundle their own Codex copy must also be checked.
anand iyer retweeted
For those excited about abliterated models, we've been serving multiple uncensored models on DreamFort (powered by @OpenGradient)
Try them here - privately
dreamfort.ai/
anand iyer retweeted
Today is a mathematical date we won't see again for 78 years.
10/24/26
10² + 24² = 26²
It's called a Pythagorean Triple Day. There are infinitely many Pythagorean triples, but only 12 dates this century satisfy the equation.Today is the last.
Next one: March 4, 2105.
Some takeaways from LPs at RAISE:
- With everyone widening their mandate into AI, every pitch sounds identical. True portfolio concentration is the only thing standing out.
- The person taking your meeting is usually an analyst who will not be at that firm when your DPI shows up. Give them something that pays off this year. An intro, a co-invest, help getting promoted. DPI gets you the next fund. Being useful now, gets you this one.
- When an LP can pick freely between asset classes, venture loses. You stop pitching your fund and start defending the whole asset class, and that is a fight you do not win.
- An LP put it bluntly: LPs have zero FOMO right now.
Heard from VC LPs this week. It was our AGM, so I had a ton of convos. Theme is liquidity and differentiation. Some quotes 👇🏻
- “we see a huge difference btw the posture of the GPs who do public + private and those that just do VC/Growth. They are living in different realities.”
- “every GP is raising again faster than we thought, either because they just blew through the money, or because they want a war chest for when the market trips over itself”
- “Across the top GPs, everyone is in market. TVPIs look amazing - differentiation is DPI, concentration and portfolio mgt”
- “We really need the PE guys to start selling some stuff!!”
- “If Anthropic IPO goes great, we are worried it sets the bar too high for everyone else. And if it doesn’t go great, then the bar is even higher…”
