
Founder & CEO @ AGI House @agihouse_org & AGI House Ventures - working with talented ai founders and researchers to accelerate humanity’s transition to AGI
Hillsborough, CA
Joined December 2008
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Will AI save humanity or seal its fate? Experience the film at the center of the conversation.
Watch THE AI DOC: OR HOW I BECAME AN APOCALOPTIMIST at home now on Digital & Blu-ray. uni.pictures/TheAIDocX
Rocky Yu retweeted
40 projects. One day. Gemma running 100% on-device.
Hundreds of builders joined us for the Google Gemma Hardware Hackathon with @GoogleDeepMind, the builder finale to Open Source AI Week.
$60K in cash prizes. Here’s what they built: 🧵
Rocky Yu retweeted
Hardware Partner Spotlight: NVIDIA joins The @Google Gemma Hardware Hackathon @ AGI House!
Build with Gemma. Run it locally on @NVIDIARobotics Jetson. Bring your next edge or physical AI idea to life.
On October 24, join 100 selected builders, the @GoogleDeepMind Gemma team, and hardware partners for a full day of hands-on building—from camera assistants and intelligent edge devices to robotics applications.
🤖 NVIDIA hardware available to borrow during the hackathon:
• 15 Jetson Orin Nano Super developer kits, each with an NVMe SSD
• 15 Jetson Thor developer kits
These kits are available for on-site development and must be returned after the event. Availability is limited.
🏆NVIDIA hardware awards are also in the works—stay tuned for the final prize details.
Every project must use a Gemma-based model and run entirely on-device. Bring your laptop, your ideas, and your robot if you have one.
Compete for a share of $45,000 in cash prizes, plus partner hardware awards.
📅 Saturday, October 24, 2026 · 10 AM–5 PM PT
📍AGI House · Hillsborough, CA
Hosted by Google DeepMind and AGI House
Apply to build → agihouse.org/events/the-goog…
#Gemma #NVIDIA #Jetson #EdgeAI #PhysicalAI #AGIHouse
Rocky Yu retweeted
1) Trying again.
TLDR: Everyone can win. Open-source, frontier labs, infrastructure, applications and most importantly the American people.
I understand all of the jitters about Anthropic losing share, real rates, spreads and second derivatives. Still think the most likely future outcome is a hybrid, multi-model future where almost everyone wins, open-weight tokens are likely the overwhelming majority of tokens, the dominant frontier model companies still do well albeit perhaps not the outcome that seemed possible in May and AI infrastructure/applications benefit immensely from the increased fragmentation, competition and lower margins at the model layer.
Lower margins at the model layer mechanically drive higher compute demand as long as someone is willing to pay for the training. And Jensen is obviously willing to be the “trainer of last resort.” As below-market compute contracts signed in 24/25 roll-off and reprice higher, cloud and neocloud revenue should continue to accelerate which will help address financing fears; I think we may be near peak external financing for this buildout. Noteworthy that using data from 3Fourteen Research, B200 clusters will generate a 37% IRR *if* their rental price declines 40% per year. Prices are still going up, but that IRR math is too ridiculous to print. 37% IRRs available at scale should get funded.
Re: this weekends controversy about MOUs, Anthropic’s $413 billion of real contractual commitments sounds scary. Reality is that it works out to roughly $31 billion of annual cloud spend, which is easily internally financed given their reported ARR, and $161 billion of spend at Broadcom which will be financed by Broadcom and private credit.
Continue to believe fragmentation at the model layer is the best outcome for ordinary Americans and humans everywhere. As I have said before, do not trust any 5-20 individuals to make the best possible decisions about the direction of this technology. I want as many AIs as possible to maximize the odds that they encompass the diversity of human values across the world and richness of human variation. And I want an AI that shares my own personal values. Safety, freedom and the maximization of human agency through democratization, distribution and diffusion vs. centralization. History has spoken on the dangers of the latter path.
Finally, from my perspective anything that slows this buildout down is net positive over the long-term as the biggest risk and worst outcome is an *actual* overbuild. We are structurally short watts, wafers and increasingly capital. That is all good. God forbid we were still in ZIRP, TSM ramped capex to 3x their current plan and we weren’t short turbine blades: there would almost certainly be an overbuild and a bubble. That would be terrible. Slower and smoother for longer is better both for investors and for the world. A terrible cyber event is the most plausible near term bear case IMHO, but AI cyberdefense ramping quickly and I was happy to hear Sholto say that cyber probably becomes defense dominant in the near future.
Longer, threaded version follows:
Rocky Yu retweeted
Ready for The Living Internet Build Day tomorrow? Check out our pre-event memo for tons of doable project ideas you can prototype and ship in a single day.
Just realized Alex @alexmashrabov was on the attendee list for our @agihouse_org "Build, Test & Launch Your LLM Apps" hackathon back in 2023!
agihouse.org/events/BuildTes…
A year ago we were talking about $1B ARR by EOY 2026 — you hit it 3 months ahead of schedule. Congratulations, and an honor to be a friend and investor. Now onto the $1T company @higgsfield_ai !
Today Higgsfield crossed $1B in annualized revenue.
We've grown 20x since September 2025.
There’s a popular argument that foundation models will capture all the value in AI, leaving little for the application layer.
Higgsfield is proving the opposite thesis, as better models expand what enterprises can achieve and increase the value of applications embedded in their workflows.
That thesis is reflected in our business:
300% net revenue retention.
115% MoM growth in enterprise adoption since June.
Positive gross margin since the beginning of this year.
Thank you to our incredible team and everyone building it with us.
PSA: AGI House is an applied AI lab and venture capital firm advancing frontier AI to accelerate humanity's transition to AGI. We host events at our Hillsborough, CA headquarter.
To be clear: there is no AGI House residency, and we have not been a hacker house since 2024.
I’ll be hosting the CEO of @NEC at the event to talk about enterprise AI agents this Saturday, space is limited, welcome to rsvp!
Enterprise AI pain point: not the model, but post‑demo delivery. Around 90% agent pilots fail production: security blocks, tool‑call instability, CIO pricing gaps. 17% enterprise agent deployment now; 60%+ target rollout in 2 years.
AGI House is bringing together the best builders for a one-day sprint on taking agents inside real companies, with legacy systems, regulated data, and real accountability. We'll be working on agent identity and permissions, evals that catch failures before customers do, human-in-the-loop controls that regulators accept, and the forward-deployed patterns that turn one working pilot into fifty.
🤝Sponsor: @altmansolon
📣Speaker: @dawnsongtweets , @rolandgvc
💰Prizes: $5k cash + exa credits.
🙌Come and join: agihouse.org/events/enterpri…
Rocky Yu retweeted
Replying to @neilmovva
@neilmovva , co-founder and CEO of Sail Research, is building some of the most efficient inference infrastructure in the world for AI agents that run autonomously over hours, days, or even weeks. Sail's platform lets developers run agents inside customizable virtual machines called Sailboxes, and recently made headlines for setting a new high score on BrowseComp-Plus.
In this conversation at AGI House, Neil breaks down what efficiency really means across the AI stack, from chip selection to inference engines.
We discuss:
The Thesis: Infra Built for the Wrong Future
Product Deep Dive
Customers, Competition & the Agent Economy
The Raise & Company Building
The Long View
Timestamps
00:00 Introduction
01:14 What does a GPU look like physically?
02:03 Inisghts into Neil and his co-founder
05:34 The drive for efficiency
08:30 The future Neil is building towards
13:00 Where is money being spent?
16:20 Being creative with chips
19:02 Tradeoff of efficiency for speed
24:51 Why Sailboxes are free
27:17 Inference is much more scalable
28:51 Neil’s honest thoughts on different models
33:36 The two axes of efficiency
36:49 Customers using Sailbox
41:35 How Sail competes with other AI companies
46:46 What Sail is prioritizing in research
51:29 Who are you hiring?
52:48 The future of technology and its impact on society
57:43 Rapid-fire questions
Rocky Yu retweeted
Tomorrow’s our Long Horizon Agents Build Day! 🙌To get ahead of the game, our pre event memo shares loads of doable project concepts you can build in a day!
Rocky Yu retweeted
Listen to my conversation with @rockyrmit on @agihouse_org
Spotify: bit.ly/3v1R0Tu
Apple: bit.ly/4bTCwpD
Youtube: bit.ly/3uXthnv
LinkedIn: bit.ly/3Xs8GQP
Website: svppro.com
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#ArtificialIntelligence #AGI #Startups #AIInfrastructure #FutureOfWork
Rocky Yu retweeted
Public Service Announcement for AI Founders: 8 months after selling @CleanlabAI
AI Infra ≠ Infra for AI
"AI Infra" startups build/sell AI (train models, research)
"Infra for AI" startups build/sell infra used by AI (monitoring, scaling)
There are 4 gold lanes of "AI Infra"..
Rocky Yu retweeted
More inference, more experiments. Everything we learned from hosting the Auto-Research Summit, in one recap:
Rocky Yu retweeted
Voice Agent Build Day is tomorrow. If you want a head start, our research brief covers the full voice agent stack plus project ideas you can build in a day:
Rocky Yu retweeted
Training isn’t the only scaling law anymore.
Jerry Tworek @MillionInt of @coreauto explains why inference-time compute is becoming the new frontier. Full keynote in the replies ↓
Rocky Yu retweeted
Frontier Notes from AGI House: Jerry Tworek on why “the era of evals is done,” and what code, RL, tool use, and real-world feedback teach us about the path to automated AI labs.
Rocky Yu retweeted
Frontier Notes from AGI House: Jerry Tworek @MillionInt on why “the era of evals is done.”
𝐅𝐮𝐥𝐥 𝐢𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 ↓
Jerry Tworek spent seven years at OpenAI, where he worked on research that helped shape modern coding and reasoning models, including Codex and reinforcement learning for reasoning.
He later served as VP of Research, and is now CEO of @coreauto, building toward automated AI research.
At AGI House, Jerry sat down with @rockyrmit for a technical conversation on Codex, HumanEval, Copilot, RL, tool use, test-time compute, and automated AI labs.
We cover:
› Why code became the first serious LLM vertical
› What HumanEval taught the field about verifiable rewards
› Why Jerry thinks “the era of evals is done”
› How real-world deployment differs from static benchmarks
› What GitHub Copilot taught OpenAI about product quality
› How reinforcement learning shaped modern reasoning behaviors
› Why tool use is “99% systems and 1% algorithms”
› Whether test-time compute still has room to scale
› What it takes to build automated AI research labs
Watch the full interview below.




















