
Public markets + late stage VC investing @OctahedronCap. Previously same @ Tencent 腾讯投资. 🇺🇸🇨🇳🇮🇳 since birth. NerdNation 🌲 @Stanford is home.
San Francisco, CA
Joined April 2009
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Chris Huskey retweeted
Ali Ghodsi never wanted to be CEO. In 2015, he was interviewing for a professor job at Berkeley when the @Databricks board handed him the interim title. Revenue that year was $1.5M.
This episode with @alighodsi will go down as one of my favorites. 10 things I took away:
1. Focus the entire company, orders of magnitude of attention, on its single biggest bottleneck. Like a laser, almost to an extreme. The cycle is 1-3 years, not weeks. If your focus changes weekly, then you’re just in firefighting mode.
2. There is nothing worse than a conflict-averse CEO. They are wonderful people, but they are in the wrong job. Conflict is the gym for a CEO: nobody likes it, but everyone has to go.
3. Study your enemy carefully, understand their weaknesses and apply your strengths to those weaknesses. Snowflake had 2x his revenue. He didn’t copy them. He found three weaknesses (proprietary, no AI, expensive) and hammered them account by account for four years. Watch the competition, never follow it.
4. The concept of a Lakehouse was ridiculed internally and online. No one wanted to market with this new term. So he made the whole company religious about it anyway, killed the ads that converted better without the word, and put it in the sales comp plan. It worked. All hands on deck, no exceptions.
5. Be willing to take a step back for a much bigger vision, even when the company is already succeeding. At multiple hundreds of millions in ARR, he was unhappy, because the vision he pitched investors wasn’t the company he was running. So he took one step back to go ten forward.
6. On the flat org, player-coach model that a lot of people have talked about this year: “it’s BS.” Separate how the company thinks (AI, ontology) from how the humans get managed (they are after all, still humans). His staff meets 3x a week. I asked if it could just be coordinated in a Google doc. His answer: do you meet your wife and kids, or coordinate that in a Google doc?
7. His test for a sales leader: can they build the car, or just drive it? Ron Gabrisko, the Databricks CRO, had seen $0→50M and $50→100M+, and hadn’t changed jobs in 10 years prior to joining. Now he has been the CRO for over a decade. He built and drove the car the whole way. That almost never happens.
8. Hire execs ahead of the curve because by the time you need them, it’s too late. A real search takes 6-12 months. The extra time helps you increase false negatives and decrease false positives. Do an insane number of backdoor references because 80% of ‘front door’ references are bs.
9. The best salespeople are not super technical, so stop trying to force them to be. Square peg, round hole. The best win with professional aggression, high EQ, and mapping the real power base (how decisions get made high up in an organization), not technical depth.
10. Yes, your best AEs will annoy people. One of the first at Databricks got a meeting nobody could get, but got banned from the customer’s building for it. He told Ali, “what are you complaining about? I got the meeting.” Professionally aggressive is the bar.
One bottleneck, zero wussing out. He reminds me of @elonmusk that way.
Chapters
0:00 – Introduction
1:22 – The secret CEO search and why the board bet on a founder
4:11 – Professor or CEO? Always taking the harder option
8:18 – Pour everything into one bottleneck
12:16 – Killing PLG and learning what great enterprise sellers actually have
19:09 – Hiring ahead of the curve: sales leaders, execs, and back-door references
27:02 – The Snowflake rivalry: study your enemy, never copy them
33:22 – Lakehouse: conviction, ridicule, and the case for second acts
41:02 – The killer instinct and why conflict-averse CEOs fail
44:10 – Dunbar's number and rethinking the org chart around AI
48:00 – AGI is already here — enterprises just use it as a chatbot
52:55 – Does he still code? Two days for a connector vs. three quarters
57:18 – A day in the life, and why the Monday meeting isn't theater
1:04:53 – Why Databricks will go public, just not yet
1:08:09 – Get over conflict aversion, or don't be CEO
1:11:07 – Brian's takeaways
Link to more in the comments.
Chris Huskey retweeted
disturbed by the confidently held opinions and lack of curiosity from tech leaders, politicians, vc’s on the pacing the frontier topic.
if you’re not building the frontier yourself, how can you possibly have a strongly held opinion on how bad the alignment problem is and what’s coming our way and what the right policies are?
now is the time to listen with big dumbo ears. i have been talking to research friends all weekend and the fear is sincere. i’m sure there are 4D chess moves and hidden motives, but the fear is sincere.
i for one don’t have a strongly held opinion, other than that now’s the time to listen with curiosity instead of judgment.
Chris Huskey retweeted
Imagine replacing every employee at American Airlines, Home Depot or Medtronic with a math olympiad who approached every task as a problem of discovery. Costs would not fall, but would rise rapidly and dramatically as every routine decision would be re-examined from first principles. The organization would drown in intelligence it cannot productively use or absorb
Okay, I keep trying to correct Claude's gratingly awful speaking style (a weird mix of over-dramatic wordiness + unnecessary terseness) and I can't. It seems to be too embedded in the model. How do all the people at Anthropic who work on this model use this thing all day??
(*ZDR = Zero-day Retention of your data. Essentially, no ZDR option means your data from Fable usage is just retained by default whenever you use the model, even if you’re an enterprise, because they don’t even allow the option to require that your data gets discarded after each API call.
For example: We can’t even have Fable as an escalation option anywhere in our Octahedron agents stack, because there’s no ZDR option at all (through model gateway or Anthropic API direct).
….Then again, does anyone even need to escalate anything to Fable anymore?
It only took 2-3 more weeks for GPT-5.6 + Kimi K3 + Thinky @thinkymachines to make this not really an issue anymore… 🎉
Anthropic changing its approach to make Fable no-ZDR-only (i.e., retains usage data)* is somehow still criminally under-recognized outside the dev. world.
@Suhail nailed it here.
If your core technology continues to get commoditized (i.e. Kimi 3), you must go up the stack. The labs will continue to encroach on everyone in the app layer. You are their competition now.
I would be very careful to concentrate on them and send them your trajectories! Figma -> Claude Design is just the beginning--expect 20+ of these.
How your users navigate and use your products is an invaluable dataset. With the advent of Fable 5 not practicing ZDR, you should be super aware of this.
OK @AnthropicAI @bcherny now this (v1.11187.4) is getting to be just a *little* too extra from you all.
Claudeception™:
Snowflake $SNOW CFO just now:
"You're absolutely right! Our AI products..."
Chris Huskey retweeted
SpaceX drops the hypiest IPO filing and says "we believe we have identified the largest TAM in human history", and you're like, my interplanetary east India company, what delectable spice have you decided to ship across the stars, and it's like... 22 trillion dollars of b2b saas
One of the better descriptions of models’ extreme jaggedness:
“You're either in the data distribution (on the rails of the RL circuits) and flying, or you're off-roading in the jungle with a machete (in relative terms).”
Fireside chat at Sequoia Ascent 2026 from a ~week ago. Some highlights:
The first theme I tried to push on is that LLMs are about a lot more than just speeding up what existed before (e.g. coding). Three examples of new horizons:
1. menugen: an app that can be fully engulfed by LLMs, with no classical code needed: input an image, output an image and an LLM can natively do the thing.
2. install .md skills instead of install .sh scripts. Why create a complex Software 1.0 bash script for e.g. installing a piece of software if you can write the installation out in words and say "just show this to your LLM". The LLM is an advanced interpreter of English and can intelligently target installation to your setup, debug everything inline, etc.
3. LLM knowledge bases as an example of something that was *impossible* with classical code because it's computation over unstructured data (knowledge) from arbitrary sources and in arbitrary formats, including simply text articles etc.
I pushed on these because in every new paradigm change, the obvious things are always in the realm of speeding up or somehow improving what existed, but here we have examples of functionality that either suddenly perhaps shouldn't even exist (1,2), or was fundamentally not possible before (3).
The second (ongoing) theme is trying to explain the pattern of jaggedness in LLMs. How it can be true that a single artifact will simultaneously 1) coherently refactor a 100,000-line code base *and* 2) tell you to walk to the car wash to wash your car. I previously wrote about the source of this as having to do with verifiability of a domain, here I expand on this as having to also do with economics because revenue/TAM dictates what the frontier labs choose to package into training data distributions during RL. You're either in the data distribution (on the rails of the RL circuits) and flying or you're off-roading in the jungle with a machete, in relative terms. Still not 100% satisfied with this, but it's an ongoing struggle to build an accurate model of LLM capabilities if you wish to practically take advantage of their power while avoiding their pitfalls, which brings me to...
Last theme is the agent-native economy. The decomposition of products and services into sensors, actuators and logic (split up across all of 1.0/2.0/3.0 computing paradigms), how we can make information maximally legible to LLMs, some words on the quickly emerging agentic engineering and its skill set, related hiring practices, etc., possibly even hints/dreams of fully neural computing handling the vast majority of computation with some help from (classical) CPU coprocessors.
Chris Huskey retweeted
Bare metal infra double backlog and growing 50%+ on multibillion scale.
Meanwhile, SaaS CFOs be like "well technically, our growth did not decelerate since if you round up the decimal of 9.55%, its basically 10% once you adjust for FX tailwinds"
Chris Huskey retweeted
It took us 47 iterations to stop one of our AI SDRs from being too aggressive on pricing. Not 3. Not 10. Forty-seven.
Each iteration required reviewing outputs, adjusting prompt instructions, testing again.
That's not a bug in AI GTM. That's the job.
The companies failing with AI agents are the ones who expected 2 iterations. The ones winning expected 50.
Every single line of this is spot on, h/t @mckaywrigley. Could not have said it better point-by-point.
some random ai thoughts:
- for code, i went from 80/20 claude/gpt to 80/20 gpt/claude in <3 months. surprised by this tbh, and interested to see where the split is at in another 3mo.
- claude still mogs gpt for non-coding agent stuff. codex feels like an engineer (which is great for coding!), whereas claude still feels like a general purpose coworker. gpt still lacks that coworker magic
- i’m pretty meh on opus 4.7. my experience hasn’t been *bad*, but it certainly hasn’t been good. sideways if anything.
- anthropic has got to figure out the compute thing. you can feel it as a user. vibes are all out of whack bc of it. my opinions above are all likely downstream of this. it’s an issue.
- anthropic labs continues to be the goat of ai product. claude design is another hit. it’s fantastic. idk why it’s not talked about more? a+
- updated claude code app is great. i finally switched out of the terminal for it. very well done.
- how are people STILL sleeping on the claude agent sdk? i feel like i’m going insane.
- gpt 5.5 is incredible. the level to which i trust it for engineering is amazing. if i could only have one model rn, it would be this one just bc of strong need for the coding use case.
- codex team is killing it. app has been the gold standard since 5.3 release (buuut i credit conductor team for the ui innovation that everyone is using now). though i could do with a little less passive aggressive shots at ant from the codex team. TARS, dial up class by 30%. it’s a long race guys haha
- i uninstalled cursor this month and am now back to vs code for my ide. composer just can’t hang with claude/gpt, and the product feels a bit all over the place. pretty stoked about the xai thing though, because their team is absolutely stacked and i’m excited to see what they might be able to do with that compute. codex and claude code are t1, cursor is t2. i would love if this deal got xai/cursor to t1 for a real trio there.
- gemini…? seems like this is 2-3 models now where the model seems like a great release and then nobody ever uses it? i’m bullish google/deepmind but weird it hasn’t translated to product use in any form. kinda disappointed still
- no open source models have hit the opus 4.5 level. was hopeful the new deepseek would get there, but nope. good oss agents will have to wait a few more months it would seem…
- Gemini/Google take nails the conflicted feeling:
"…? seems like this is 2-3 models now where the model seems like a great release and then nobody ever uses it? i’m bullish google/deepmind but weird it hasn’t translated to product use in any form. kinda disappointed still"




















