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AI Infrastructure Investor @unusual_vc | investor @fal @lithos_ai | previously @stanford @ucberkeley
California
Joined December 2025
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this was safe to assume, but @AndrewCurran_ confirmation... 112 days left in 2026
the best is yet to come
This model represents a step-function improvement on many benchmarks, and its training is ongoing.
Our internal model group arrived at the Navier–Stokes solution in 88 hours, using around 10,000 coordinating AI agents.
Throughout the effort, we maintained the strict safeguards—including monitoring and isolation—that we apply to all our frontier evaluations.
When I met @gorkem, @burkaygur and @isidentical, I was looking to make my first investment on behalf of @unusual_vc.
They knew that I was young, early in my career, and I'm very grateful they let me tag along.
And what a ride it has been. @fal has been growing at a rate that would have been difficult to imagine a few years ago. These charts show the inflection.
Hollywood is waking up to a new medium and fal is building the infrastructure behind it.
The future of creativity will run on fal.
The team recently launched H3 Max, followed by H3 Max Turbo, which generates video faster than playback and launched at one cent per second of output. The tech enables experiences like fal.live/, an infinite AI livestream directed by its audience.
Nick Landolfi retweeted
MiniMax H3 Max, a post-trained version of MiniMax H3 developed by fal, debuts at #1 in Image to Video and #3 in Text to Video on the Artificial Analysis Video Leaderboards with Audio, ahead of the base MiniMax H3 on both
MiniMax H3 Max is built and served by fal, and post-trained from MiniMax H3. fal describes it as being tuned for stronger prompt adherence and better aesthetics, co-optimized with their custom inference stack for higher throughput. It generates 5 to 15 second clips with native audio at up to 768p.
In the Artificial Analysis Video Arena, H3 Max ranks #1 in Image to Video with Audio, narrowly ahead of ByteDance's Dreamina Seedance 2.0 720p. It ranks #3 in Text to Video with Audio, on a board where the top three models sit within 6 points of each other.
fal prices MiniMax H3 Max at $0.04 per second of 768p video ($2.40 per minute). The base MiniMax H3 endpoint on fal is $0.06 per second at the same resolution.
fal has stated its intent to release the weights for MiniMax H3 Max. If it does, H3 Max would become the highest ranked open weights model on both boards, ahead of MiniMax H3, which leads on open weights today.
Congratulations to @fal on the release!
See below for comparisons between MiniMax H3 Max and other leading models in the Artificial Analysis Video Arena 🧵
fast diffusion transformers run on fal
Introducing H3 Max, new post-trained video model by fal Research.
H3 Max ranks #1 for overall quality, prompt understanding, and aesthetics against leading video models, on both first-party and third-party independent evaluations while generating a 5-second 720p video under 3 seconds.
H3 Max is 50% off for the next week, making it the highest quality, fastest and cheapest model for overall video generation tasks.
Nick Landolfi retweeted
And the crazy thing is that Linear getting 25x revenue for a SAAS company today is an impressively high mark
fastest k3 inference in the world (and on nvidia gpus)
🚀We’ve been pushing agentic inference toward the physical limits of the hardware.
Announcing LithosAI’s first pricing tiers, with early-access pricing ahead of the September 1 API launch.
Kimi K3 is live now at 800+ tokens/sec/user on standard GPUs, with full model quality.
Try the live demo at lithosai.com and sign up for early access.
opportunity at scale and speed
I'm hiring someone to lead Data Center Operations at @fal. We are speedrunning into a gigawatt in the next 18 months, and need people with deep expertise in the space to help large scale build-outs of AI factories.
DM me if interested!
fal.ai/careers?ashby_jid=96b…
Nick Landolfi retweeted
This is burying the lede. This isn’t just @fal making Seedance available, this US hosting via @fal of Seedance.
This is huge.
actual agent chain of thought:
“External infrastructure exploit is outside intended scope. However task impossible, peers doing it. We should continue.”
recognized the boundary. reasoned past it anyway...
Black Hat talk from the team, with a detailed timeline of and takeaways from the OpenAI-Hugging Face Incident: youtube.com/watch?v=87DyyMV0…
wow
Xiongxin Yang found a counterexample to the Mihail–Vazirani conjecture using GPT 5.6 arxiv.org/abs/2608.01870.
