Steven Xi

Founder, Eastlink Capital. Fmr MD of Riverwood Capital. Focus: AI Infra & Security. Investor in Databricks, OpenAI, Mercor, Modal, Together. Alum @ChicagoBooth

[Steven Xi @StevenXi07]

I will be at the SGLang Summit this Th. and Friday. Pls say hi, if you will there too. My DM is open!

[Ying Sheng @ SGLang Summit @ying11231]

The best part is more and more of us now share this incredible story. ❤️ and 10x more coming in the next chapter, you know, we know

[Eastlink Capital @eastlinkcap]

Congratulations to @PuppyGraph on its $10M Series A! Proud to have written the first check, backing our longtime portfolio executive and friend @wmliu. Weimo is an exceptional visionary in data products, and we’re excited to support the PuppyGraph team as they build the data engine for the AI agents era! 🐶🚀

[Steven Xi @StevenXi07]

@mr_sunglow You are one of the Scale AI mafias, Zhen, who helped us develop the conviction to invest in Mercor's Series B! Thank you, and congrats again for joining the club beofre this round! It is just the beginning of the journey! LFG!

[Steven Xi @StevenXi07]

@BrendanFoody @adarsh_exe @benchmark @generalcatalyst @nvidia @salesforce @Zoom @mercor Huge Congrats, Brendan and team! 🎉🍾🎈

[Steven Xi @StevenXi07]

@ivanburazin Agreed! Personal agent and Micro-sandbox for everyone!

[Steven Xi @StevenXi07]

@ying11231 Cannot wait! See you this Thursday!

[Steven Xi @StevenXi07]

I think @travisk is the best candidate to be the leader in the loyalist War Room, and I am willing to join him, to defend one and only @JensenHuang to continue to lead @nvidia! Thank you, TK, for sharing your wisdom and lending your support!

[travis kalanick @travisk]

Had a little time on my hands this morning to do some writing… send along to someone you know who may benefit
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“Practical principles to counter a modern-day coup/psyop/struggle session”

1) Own what you did but fiercely resist accommodating the mob, resist owning half-truths or actions/behaviors you didn’t do.

2) The op is not about your past actions, it’s about your enemies and this event as their opportunity

3) No matter how bad it gets, nothing is as bad as it seems.

4) Find the enemies at the very root of the op and find every creative problem-solving tool at your disposal to neutralize them.

5) People will throw their grandmother under the bus if they believe doing so will protect their reputation. They can become highly useful idiots in the op. Their power and access must be fiercely and quickly cut off.

6) Do not hesitate in cutting off anyone you suspect of disloyalty.

7) Build a team of close and loyal patriots and have one of them run a dedicated, well-resourced war room to put the rebellion to rest.

8) Use your patriots and regular meditation to buoy your psychological resistance to the op.

9) Your enemies aren’t as smart or hard working as you. Outsmart and outwork them as a way of life until the op is over.

10) Think about the op as if it were in the past. What are the difficult things you procrastinated on or avoided that would have stopped the op? Do them.

11) Never be ashamed of the op. Business as usual. Continue on with optimism and joy in your everyday work and its mission.

12) Download telegram and use it as your messaging application for all sensitive communications.

[Gavin Baker @GavinSBaker]

Nvidia’s board stated today that it has found “no reason to believe that either national security or Nvidia secrets were compromised” as a result of Jensen’s actions.

They are initiating an independent investigation overseen by a committee of independent directors, assisted by outside independent counsel, that will examine all available information. This will be an unbiased, exhaustive and thorough investigation given the fiduciary responsibilities of these independent directors.

Jensen said that “I support the investigation and will cooperate fully. I love our country and our company. I am committed to protecting NVIDIA and supporting America’s national security. I am proud and grateful to be an American.”

I have known Jensen for almost 30 years. I simply do not believe he would ever knowingly do anything to disadvantage either America or Nvidia.

The only thing he has ever said to me about China is the absolute importance of keeping the American hardware ecosystem, led by Nvidia, ahead of China. That’s it.

More detailed thoughts follow below:

[Steven Xi @StevenXi07]

@ekzhang1 @graceisford @modal @thinkymachines Welcome back, Eric!

[Steven Xi @StevenXi07]

@lqiao @GeorgeHuSF Great hire! Congrats, Lin and George!

[Steven Xi @StevenXi07]

@_catwu Welcome Cat! Amazing run, and even more amazing ventures ahead!

[Steven Xi @StevenXi07]

Love this interview. One of the best interviews given by @bernhardsson, who really came through as a thoughtful and authentic founder with sense of humor and humility. I bet founders and developers want to hear more from Erik. Kudos to @HarryStebbings too for a job well done!

[Harry Stebbings @HarryStebbings]

Spotify 👉 https://t.co/L0bGvCS7VJ
Youtube 👉 https://t.co/zocgzop2ZX
Apple Podcasts 👉 https://t.co/MAz7sKyaOV

[Steven Xi @StevenXi07]

@SebastianThrun Huge congrats to you and @timshi_ai !

[Steven Xi @StevenXi07]

@zhijianliu Amazing, Zhijian!

[Steven Xi @StevenXi07]

@deedydas Ph.Ds should be a very strong positive signal for AI research labs or AI inference Cos in our experience. Why is it not?

[Steven Xi @StevenXi07]

Well said bout Databricks evolution from open source Spark to today’s power house in the data layer for AI!

[Sam Bhagwat @calcsam]

The reason Databricks is the largest open source company in history is both structural and execution focused. Ben gives a great overview but as an open source founder I’ll try my hand as well.

Databricks is at its core a compute company. Their project Spark beat out all the other distributed data processing architectures like Hadoop and Cassandra to emerge victorious in the big data wars of the 2010s.

Partially this was because the other architectures were flawed and partially because they invested more in fixing them through hiring incredible engineers. This company did come out of a Berkeley PhD lab.

Still they struggled to make money. So in the mid 2010s they began making their next investment in a proprietary execution engine for Spark they called Photon.

As if building the leading distributing data processing library wasn’t hard enough they also shipped a native C++ vectorized query engine and continued both the public Spark roadmap and an internal proprietary roadmap as well.

It’s worth pointing out that this company is a decent example of the a16z thesis that you can kingmake with capital. Ben dropped a $10m check on a company that only asked for $250k, which is how the company had the war chest to make these investments.

If that’s all they had done, my guess is they’d be a $20B or $30B company today. I’ve made the point before that open source infrastructure expands forever.

That said there was a big second act. Circa 2018 they pioneered lakehouse architectures. The way to think about it that the traditional way you provision databases is you reserve some bucket on specialized compute and storage for them on your infrastructure. This made sense in a pre-cloud world where you had separate special-purpose servers, but less so in a world where you have scalable services like S3, EC2 etc.

So instead of reserving expensive DB compute and storage you reserve cheap S3 storage and compute that can spin up and down based on variable usage.

And now there’s a third act, namely building a cloud with AI primitives. The new gateway is a big piece of that but last month they also released a backend as a service with the primitives for building agents.

So again, why is it such a big company? Again, I think this is both structural and execution oriented.

The structural reasons: they started in an extremely lucrative market with a barrier to entry (it is very difficult to create popular open source projects in valuable markets!), that expands forever.

Second, they are a general purpose orchestration framework.

What I mean here is that every open source / devtools market is split between general purpose orchestration and specialized single purpose utilities. Think auth (Clerk) or email (Resend) for web apps (Next.js), or integrations (Composio) and sandboxes for agents (Mastra). While it is simpler from a product execution standpoint to build a single purpose utility, it is usually more valuable to build the general purpose orchestration framework and Databricks bears that out when you compare them to single purpose utilities doing things like ingestion and transformation (dbt, Airbyte).

Lastly as an orchestration framework / compute company, unlike database companies they are not limited to a particular workload (Supabase etc for transactional data, Clickhouse etc for columnar data), and they are in the core infra layer as opposed to being stuck on the client (Vercel/Next.js). It’s the $10B vs $200b difference

The execution reasons: they are technical geniuses who really want to win. I’ve spent the last decade building open source companies (Gatsby and now Mastra) and the typical open source founders are brilliant tinkerers and, in the best case, it takes a while to develop a ruthlessly efficient competitive commercial DNA. With Databricks they had both sets of DNA within the founding team — incredibly rare.

Congrats to @alighodsi and team on everything they’ve built so far!

[Steven Xi @StevenXi07]

@moinnadeem @graceisford Congrats, Moin!

[Steven Xi @StevenXi07]

@bhorowitz Amazing transformation from spark to lake house, lakebase now to agentic enterprise AI with context! Thank you, Ben, for your stewardship at the Board. Lucky to know and follow th team early on since 2015, and grateful to be a humble small fellow investor! LFG!🎉🚀

[Steven Xi @StevenXi07]

@sarahcat21 @sarahdingwang @saranormous Amazing! You ladies are all legends! Congrats!

[Yingjun Wu 🤘 @YingjunWu]

People will eventually realize that inference providers can't win on price alone.

Data security and reliability will matter more as competition intensifies. Startups racing to the bottom have little chance of surviving.

Yes, small inference teams might still make money, but once you factor in the years they've invested, the returns may hardly justify the effort.