Max Davis

[Leo Polovets @lpolovets]

Going from initial funding to manufactured AI inference chips and racks in three years is an insane accomplishment. I'm excited to see the impact as an AI consumer in the coming months.

[Sumer Sao @sumersao]

personal update: I joined Etched.

I first met @robertwachen in early 2023 when I was a @neo scholar and he was a scholar finalist, one of my several interviewees.

I still remember our first zoom. Within two minutes of meeting I knew Rob was not an ordinary 19 year old Harvard freshman. We hit it off I quickly gave him a strong yes on our internal rubrics. I never could have imagined what that serendipitous interaction would turn in to.

[Matt Shumer @mattshumer_]

Nvidia is worth $5T because the world realized inference is the new oil.

If Etched’s chips are meaningfully better than Nvidia’s for inference…

What is Etched worth?

This is a worldview-shattering event.

[Ribbit Capital @RibbitCapital]

Every Ribbit investment starts with one question: can we explain the “why” on a napkin? As @Etched announces its latest funding round and shares more of what they’ve built, here’s what our Etched napkin says.
@UbertiGavin @robertwachen

[Jerry Tworek @MillionInt]

Chips are a substrate for computation that the layer of intelligence we're all building relies on.

Like every layer of the stack, it requires dedication, hard work, taking risks and innovating.

Etched Sohu has all those in spades and I'm impressed every time I hear about clever solutions that went into building this chip.

I'm proud to be along for the journey. Etched team is doing something right and hardware is very difficult.

[Bryan Johnson @bryan_johnson]

Breaking news for people who want to look hot, be young and not die. 

A few years ago, two college dropouts told me they could accelerate longevity by building a faster AI chip. 

I invested, and they just pulled it off.

What it means:

> 10x more throughput (tokens per second) for the same power footprint

> Dramatically lower operational costs for executing today’s frontier models

> Run far larger, more capable AI models within the same power and thermal budget, because a transformer-specific chip spends a fraction of the energy per token that a general-purpose GPU does

Rob and Gavin's approach resonated with me because solving aging is a gigantic combinatorial search problem. The chemical space of small, drug-like molecules has around 10^60 possibilities.

These compounds need to be mapped against a human proteome derived from 20,000 genes, including 1,600 transcription factors, and a dense web of interactions among them. 

The size of the combinatorial space is problematic. You need to identify which targets to modulate, within specific cellular lineages, at exact dosages, and in optimal temporal sequences.

Traditional high precision physics simulations are too slow to brute force the problem. You can shortcut it with AI inference, using frontier neural networks as hyper fast surrogate models to predict biological interactions instantly. By hardwiring transformer logic into silicon, Etched offers the infrastructure needed to run these massive biological foundation models at scale.

I'm surprised and impressed they were able to pull this off, and so quickly. 

They already have $1B in orders

[Tri Dao @tri_dao]

It's wild how quickly Etched designed and got the chips out, all within 2 years. They went deep, hardcoding attention into silicon and getting very high MFU. This kind of hardware tailored made for LLM inference is soon gonna bring cost of intelligence down 10x

[Etched @Etched]

https://t.co/DuD7DmWRSj

[Gavin Uberti @UbertiGavin]

Seeing our A0 silicon and first-gen racks come to life has been the experience of a lifetime. It takes a village - I am so proud to be working alongside @robertwachen @czhu1729 @saptadeep_pal and our world-class team to do what many thought was impossible.

We think our Low-Voltage Inference (LVI) and Cluster-Scale Memory (CSM) tech will help bring down the cost of inference for the world, all while using less power. But there’s still much to do on next-generation products - if you like solving hard problems, join us!

[Robert Wachen @robertwachen]

Bringing our first rack to life has been nothing short of exhilarating and grueling.

@UbertiGavin @czhu1729 and I hibernated in San Jose for three years building the team, solving thousands of problems, and convincing the world to believe in us.

I'm excited to finally start sharing what we've built. I think you'll love it, and we're just getting started:

[Chris Zhu @czhu1729]

Three years ago this was a handful of us and a bet that felt obvious to us and crazy to most: the world was going to need vastly more inference than anyone was building for, and the systems to serve it didn't exist yet.

Today it's 400+ of the best engineers I've ever worked with, real silicon, and racks shipping this summer.

Building frontier inference systems is hard. AI moves fast. Doing both at once takes an absurd group of people, and somehow we found them.

Grateful beyond words to my cofounders @UbertiGavin and @robertwachen and the whole team. Incredibly proud of what everyone has put into this. The hardest and best part is still ahead.

[Chubby♨️ @kimmonismus]

Etched is coming out of stealth with $800M raised, $1B+ in customer contracts, first racks shipping this summer, and claims of SOTA inference throughput, latency, and power efficiency.

But holy, look who backed the funding. The who-is-who if AI reseracher and VC.

[Etched @Etched]

We're coming out of stealth.

We've built our first racks after a successful A0 tapeout, $1B+ in customer contracts, and $800m raised.

Early customer tests show us achieving SOTA throughput, latency, and power efficiency on inference workloads.

Our first racks ship this summer.

[Patrick OShaughnessy @patrick_oshag]

Three years ago, two Harvard dropouts set out to build a better AI chip than the largest companies in the world.

Almost everyone I called at the time said it was impossible.

Today, Etched (@Etched) comes out of stealth with $800M total raised, $1B in signed customer contracts, and a working next-gen AI chip.

This was my excuse to ask the two founders, @UbertiGavin and @robertwachen, every question I have about compute and inference.

We discuss:
- Why they built an entire rack and not just a chip
- The two technical bets behind their architecture no one else has tried
- How two founders in their twenties recruited industry legends
- The night they nearly ran out of money
- Why whoever produces the most tokens wins

If you care about the future of compute, Gavin and Rob are two people to know. I think you will find the story of what they have built hard to forget.

Enjoy!

TIMESTAMPS
0:00 Intro
1:00 Why Nobody Believed Etched Would Work
14:06 Why Inference Is the Bottleneck
22:27 Gavin and Rob’s Origin Stories
33:24 Taking Huge Risks to Move Faster
49:43 Kernels, Compilers, and the AI Stack
1:02:08 Raising $100M to Survive
1:16:00 The Future of Models, Agents, and Intelligence

[Central @WestHam_Central]

The mini-league lives on 😂

[Zac ~ VANCOUVER N3✨ @alltoozac]

Yeah they gotta do it themselves now since trump fired like 90% of the national park service

[Kevin Kelly @kevin2kelly]

I've put all my best travel tips from 53 years of traveling into one document. Enjoy! https://t.co/N46gsoD3hR

[Central @WestHam_Central]

West Ham in 2024:

Games - 46
Wins - 12
Draws - 14
Defeats - 20

GF - 59
GA - 90
GD - -31

All while getting rid of the best manager in our history.

Awful year.

Adios.

[MDP⚒️🏴󠁧󠁢󠁥󠁮󠁧󠁿🏆#SB OUT👍 @martinhammer999]

Morning all 🙂👍⚒️

[Jeff Morris Jr. @jmj]

Have always dreamed of an IMDB for tech where we actually know what projects people contributed to at companies.

As far as I can tell, every person who worked at Facebook in the early days wrote the entire codebase, launched their ads business, and created their mobile strategy.