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.
habibi's annual gathering at sierra camp is just the beginning... cc @MKlinkachorn @SasankhM
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
Move fast and break things. Startup lesson. At some point you realized you broke too much and now spend more time fixing it. Kind of each stage is different
Erfie retweeted
关于SemiAnalysis说“英伟达KyberNVL144机架架构出现大规模延迟”这篇推文，可能很多人都不理Rubin，Rubin Ultra两种芯片型号，Oberon和Kyber这两种机架型号的区别是什么？需要关注的点：
1、Rubin是GPU架构（芯片层面），搭配Vera CPU，2026下半年开始量产出货（6月初台北电脑展已经展示量产机型了）使用Oberon机架（NVL72规模）。
2、Rubin Ultra是Rubin的升级加强版（更多内存、更高性能配置），计划在2027年推出，使用Kyber机架。Kyber机架是专门为Rubin Ultra设计的新一代高密度机架，目标是把单机架GPU数量从72提升到144，通过更先进的 NVLink 实现更大的“scale-up 域”（把更多 GPU 像一个大 GPU 一样紧密连接）。
Semi说的延迟就是这个Kyber机架因为PCB中板的可制造性可能出现延迟。
3、在英伟达的规划里，Rubin Ultra NVL144(Kyber机柜架构）本身就要到27年才会推出，量产可能也就到27年下半年去了。毕竟rubin的NVL 72今年才半年才能量产嘛。其实不用过多关注kyber的进度，其实一切按照达子的预期节奏推动，Kyber量产落地也要到明年下半年去了。
4、然后SemiAnalysis说一个原本计划在27年下半年才能量产落地的Kyber NVL144机架架构要落后延迟到28年。大家都再说英伟达要延迟了🤣
重点还是关注今年Rubin NV72（Oberon 机架）量产落地的节奏。
MASSIVE DELAY: Just 3 months after Jensen demoed Kyber NVL144 at GTC, it has faced major setbacks and has been delayed by more than 12 months, pushing it back to 2028. Below, we explain why Kyber has faced massive delays and why NVIDIA’s NVL72x2 back-to-back rack architecture was also cancelled, leaving Rubin Ultra with a limited scale-up domain. 👇️ 1/6🧵
I still remember meeting Gavin when he just moved to the bay. They have come a long way. Congrats
Erfie retweeted
Pax Silica, one of the finest initiatives to drive multilateral alignment on securing the silicon supply chain, thanks to @jacobhelberg
Great to witness here in DC.
