Brendan the goat
We raised our $750 million Series D at a $20 billion valuation from @benchmark, @generalcatalyst, @nvidia, @salesforce, and @zoom.
@mercor scaled from $1M to $3B in run-rate revenue in 27 months, the fastest in history to $3B.
For 2 years, we've organized human intelligence to build evals and training data for every frontier lab.
The labs are the leading indicator for AI startups, then the Fortune 500.
Our growth has accelerated over the past 6 months as enterprises start evaluating and training their own models. For many, owning their intelligence is existential.
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when impossible becomes possible
Fearless Engineering: The CS-4 is our first engineering documentary.
Through lab footage and conversations with the engineers behind each breakthrough, the film follows the creation of the CS-4, our latest wafer-scale AI system.
0:00 introducing the engineering team
1:09 Wafer-scale history
2:28 Powering the wafer
4:00 The cooling problem
4:18 Designing the cold plate
5:11 Holes in the wafer
6:33 the alignment problem
7:47 The CS-4 PRD
8:35 Designing for deployability and serviceability
11:01 Removing the motherboard
11:25 Removing internal cooling loops
11:41 inventing the power rack
12:35 A one-person lab deployment
14:03 Backpack: 50% fewer components
14:55 Connecting the compute backpack to cooling manifold
17:58 shifting Cerebras’s focus from training to inference
19:09 Building the Cerebras Cloud
20:01 CS-3 vs. CS-4 performance
20:49 3D Stacked DRAM
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Erfie retweeted
Three pieces of fundraising advice I'm giving to founders right now.
There's a lot of talk about creating hype, but nothing can replace fundamentals. This is my sure-fire, straightforward and simple advice for pulling together a great round:
1. Hit a business milestone first.
Every company wants to hire more people and grow revenue. That isn't a reason on its own.
A good reason sounds like "we hit this milestone and we're ready for the next one." Something big enough has happened that it's worth telling people about.
When I backed @mateus_cr123 and @getenter_ai, they'd just signed their first large enterprise customer. Mateus also brought on two great co-founders, Henrique and Michael. They were ready to scale the enterprise motion. That was their compelling answer to "why now?"
2. Build relationships before you need the money.
Every investor wants to partner with a great company. Our problem is time and mental bandwidth. Founders who stand out take the time to educate us on why their company is the special one.
I got to know @alanjaimem for over a year before he raised for @Draftea_Mexico. In that time I saw how he thinks, communicates, and leads. By the time he was ready to raise, it was a straightforward yes.
3. Get smart people to understand and share your story.
Customers are some of the best people to do this. So are employees and others close to the company.
@Waymo is the best example I've seen. Long before we partnered with them, friends kept telling me about how much they loved their rides. Then I became a daily user.
When smart people understand the story and share it on their own, investors start hearing it from someone other than the founder.
That's the simplest way to create fundraising pull.
Erfie retweeted
Technical moats are mostly over at this point. If you’re building something that is meant to be durable, the only moats now are:
• Proprietary data
• Network effects
• A physical component
Pari is awesome!!
Flow has raised a $50M Series B at a $750M valuation, co-led by Antonio Gracias (Valor) and Gavin Baker (Atreides) with Sequoia Capital, Roelof Botha (SpaceX, Block), and more.
When I became a mechanical engineer, I wanted to invent. In reality, 90% of the day to day execution work (CAD, excel, sim) was digital manual labour. That’s about to flip. The engineer’s role will change more profoundly than at any point since the invention of CAD, with humans focusing on invention, architecture, tradeoffs and judgement calls, and agents taking the grunt work.
Anduril, Joby, Stoke Space, Rivian & many more - Flow is now the default platform for requirements and verification for frontier hardware teams. But it’s not just next-gen anymore. Industry giants including General Motors PPU and Volkswagen & Rivian’s joint venture (RV Tech) are reshaping their core development practices with Flow’s AI.
The revolution is just starting. Models are getting better quickly at CAD, analysis, simulation, and tool use. In the next year, AI will move from integrating the work to doing the work.
One day, this shift won't just enable us to iterate faster, it will enable us to design a class of products that we could not have dreamt of before.
We’re hiring.
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habibi's annual gathering at sierra camp is just the beginning... cc @MKlinkachorn @SasankhM
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Erfie retweeted
Today, after 7 years in stealth, I, @emeskey and our co-founders are proud to announce @keplercompute.
The world is much better off when we give power & intelligence to the people. When everyone can become their highest selves by pairing their human creativity with the exponentially rising intelligence from our magnificent computers.
Our goal is to unlock the power of frontier intelligence for everyone by pushing memory and logic chip manufacturing to the limits of physics.
To achieve this, we will move the world from one of compute scarcity to abundance where we can make orders of magnitude more chips per dollar and can lower datacenter energies to the limits of physics. We will use physics to ensure we have enough chips and that we can power them on.
Why is compute scarce?
Today's dominant approach - shrinking transistors with EUV - has run into $20B-$40B fabs that take years to build. Sized against that risk, the industry (quite rationally!) grows capex much slower than AI demand that can grow 10x in a year.
In the current system, scarcity is built in.
A different way is possible.
We founded Kepler on the following beliefs:
1. It is possible to build leadership chips without a dependence on EUV
2. It is possible to achieve multiple orders of magnitude more intelligence per watt by evolving chips 2D Silicon to 3D Beyond silicon
3. It is possible to get much closer to the physical limits of computing than we are today while staying within mainstream digital computing
Memory is the biggest binding constraint on AI compute and so we start with a new frontier of AI memory- approaching the bandwidth per watt of SRAM, going up to 10x beyond the capacity of HBM. We use 3D and materials innovations that allow for leapfrogging leading nodes while requiring no EUV lithography and allowing us use fabs that already exist.
Our team co-led one of the industry's first 3D chip-stacking technologies, has led and scaled seven generations of DRAM and multiple logic designs into billions of chips. We built the world's first commercial physical synthesis tool and techniques at the heart of the logic design industry and solved long standing challenges in ferroelectrics, invented beyond CMOS logic devices. We would love to work with you to reinvent chip manufacturing here in America.
We have raised $468m, built our own fab and our AI memories sample this year and ramp to production next year. We are allocated for 2027 and will be scaling in America by 2028.
We are open for business, please get in touch to work together. We are also growing the group of Keplerians and would love to work with you.
Learn More @ Kepler: keplercompute.com/
Read our story in @WIRED: wired.com/story/a-new-dollar…
Erfie retweeted
Today we're excited to announce Mercury 2.5
It’s the most capable diffusion LLM on the market. It is a 40% jump in intelligence over Mercury 2 and runs at over 1,100 tokens/sec on widely available @NVIDIAAI GPUs.
inceptionlabs.ai/blog/introd…
Erfie retweeted
We threw a fun event on owning your ai stack today!
80 @sequoia portfolio companies attended technical workshops on how to own your AI (models, harnesses, data, evals, RL, CL, etc). Videos and takeaways coming soon.
Towards a vibrant ecosystem for Democratized Intelligence 💚
is there a way out of @meetgranola new privacy policy? training on meeting notes not good
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