Early Stage Deep Tech Investor @ Fusion Fund

Palo Alto, CA
Joined June 2022
Ryan JB Taylor retweeted
We announced an agreement to acquire a nationwide low-band spectrum license portfolio that will pave the way for @Starlink to become a major mobile carrier in the US. With this new spectrum and our Gen2 constellation, Starlink Mobile can ensure Americans have access to high-speed mobile broadband no matter where they are → spacex.com/updates#starlink-…
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For the last decade, the commercial space industry has operated on a simple assumption: access to orbit would become cheaper, more frequent, and eventually abundant. SpaceX made that assumption credible. Falcon 9 became the default transportation layer for a generation of space companies. Now that model may be changing. SpaceX has reportedly stopped accepting new commercial Falcon 9 reservations. Existing commercial commitments and government missions will continue, but new customers are increasingly being told there is no availability. The obvious explanation is the transition to Starship. But something more important may be happening. SpaceX may simply have better things to do with its rockets than sell launches. That is already true with Starlink. Orbital compute could push that logic much further. SpaceX has said it may prioritize its own payloads over additional government or commercial customers to pursue orbital compute. If a launch can either generate a one time fee or deploy infrastructure that creates far more value through connectivity or compute, the customer is no longer just competing with another customer for capacity. They are competing with SpaceX itself. This matters for AI too. The bottleneck is becoming physical: chips, power, cooling, grid access, and data center capacity. If orbital compute becomes viable, SpaceX could gain access to a new source of compute while also owning the launch system needed to deploy it and, through xAI, one of the companies consuming it. Then consider Blue Origin. The obvious alternative is New Glenn. But Blue has proposed Project Sunrise, its own orbital compute constellation. Blue may eventually face the same choice: sell launch capacity to outside customers, or use it to build a higher value service itself. That creates a different market for space startups. There is a big difference between launch being expensive and launch being unavailable. If your business requires thousands of tons in orbit, launch is no longer a line item. It is a strategic dependency. This could also create an opportunity for independent providers such as Rocket Lab. For years, every launch startup faced the same question: how do you compete with SpaceX? The better question may become: Who serves the customers SpaceX no longer wants to serve? None of this requires orbital compute to work perfectly. Heat rejection is hard. Radiation degrades hardware. GPUs and networking equipment fail. Hardware refresh cycles are short. For those reasons, I suspect early orbital compute is more likely to focus on inference than frontier model training. But even the credible possibility changes the incentives. For years, we assumed cheaper launch would mainly benefit the companies buying launches. What if the biggest beneficiaries are the companies that own the rockets? And for everyone else: Who is actually going to launch you?
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Ryan JB Taylor retweeted
There is a very ugly truth that I think we are lying to ourselves about. 99% of startups today cannot even hire B-tier talent. The might of Anthropic, OpenAI, and the hottest of hot companies are sucking up all the A*, A, and B talent. Single greatest problem for every founder today is acquiring and retaining talent, without a doubt.
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Ryan JB Taylor retweeted
We are proud to announce a collaboration between AgileRL and @NVIDIAAI to support the post-training of Nemotron models. NVIDIA's Nemotron open-source model family is now available for fine-tuning on Arena, our platform for creating AI agents specialized at any task. NVIDIA gave us early access to their latest 30B3A parameter MoE model, Nemotron 3.5 Lightning. On Arena, we trained it to outperform Claude Sonnet 5 on two different tasks: a long-horizon reasoning challenge and a real customer support workload. Nemotron proved exceptionally easy to post-train: a single six-hour run on four H100 GPUs was enough to master the reasoning task, at context lengths beyond 50,000 tokens. The full results are in the announcement, linked below. With this collaboration, Arena customers get access to NVIDIA Nemotron models as soon as they are released, with the training and evaluation setup already built around them. Post-train Nemotron on your own data, environment and edge cases, to build an agent that masters your task. Across industries including banking, insurance, aerospace and government, production traffic is moving off frontier model APIs and onto infrastructure these companies control. Businesses want agents with genuine expertise in their specific task, trained on their own data. Security, control and data sovereignty demand that model weights remain on their own infrastructure. And they want the fixed cost of hardware they own, rather than per-token pricing that compounds with every request. This collaboration provides that path. Begin with a dataset, an RL environment, or simply a description of the job the agent has to do, and our team will build the rest with you. Arena handles the training and deploys the finished agent in one click, with the weights yours to keep.
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Ryan JB Taylor retweeted
"Dating a model" can mean one of two very different things, depending where you live in California
LA: Have your agent talk to my agent. SF: Have your agent talk to my agent.
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Ryan JB Taylor retweeted
Self recommending must-read for Canadians. “The path Canada is on, economically and culturally, is no longer sufficient to make us a flourishing world class nation.”
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Ryan JB Taylor retweeted
Just remembered the story about a computer scientist who had his bike stolen and tried to explain binary search to a cop
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Ryan JB Taylor retweeted
🚨 BREAKING: A Google researcher and a Turing Award winner just published a paper that exposes the real crisis in AI. It's not training. It's inference. And the hardware we're using was never designed for it. The paper is by Xiaoyu Ma and David Patterson. Accepted by IEEE Computer, 2026. No hype. No product launch. Just a cold breakdown of why serving LLMs is fundamentally broken at the hardware level. The core argument is brutal: → GPU FLOPS grew 80X from 2012 to 2022 → Memory bandwidth grew only 17X in that same period → HBM costs per GB are going UP, not down → The Decode phase is memory-bound, not compute-bound → We're building inference on chips designed for training Here's the wildest part: OpenAI lost roughly $5B on $3.7B in revenue. The bottleneck isn't model quality. It's the cost of serving every single token to every single user. Inference is bleeding these companies dry. And five trends are making it worse simultaneously: → MoE models like DeepSeek-V3 with 256 experts exploding memory → Reasoning models generating massive thought chains before answering → Multimodal inputs (image, audio, video) dwarfing text → Long-context windows straining KV caches → RAG pipelines injecting more context per request Their four proposed hardware shifts: → High Bandwidth Flash: 512GB stacks at HBM-level bandwidth, 10X more memory per node → Processing-Near-Memory: logic dies placed next to memory, not on the same chip → 3D Memory-Logic Stacking: vertical connections delivering 2-3X lower power than HBM → Low-Latency Interconnect: fewer hops, in-network compute, SRAM packet buffers Companies that tried SRAM-only chips like Cerebras and Groq already failed and had to add DRAM back. This paper doesn't sell a product. It maps the entire hardware bottleneck and says: the industry is solving the wrong problem. Paper dropped January 2026. Link in the first comment 👇
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Ryan JB Taylor retweeted
I can't believe humans were gifted a planet full of famine, disease, and death and invented fusion, vaccines, moon rockets, and so much food that most people die from obesity.
I can't believe human were gifted a planet with full of trees, fruit, water, animals and sunshine and then they invented debt, capitalism and war
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Ryan JB Taylor retweeted
Compliance shouldn't cost $50k. Today we're launching Universal Basic Compliance — a free foundation for companies that need to prove they’re trustworthy. • Compliance discovery • Contract requirement analysis • SOC 2 controls library • Trust Center • Cyber protection All free. Try it: koop.ai
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Ryan JB Taylor retweeted
Introducing Starlight Air, the world lightest space solar panel. Order today at terawatt.space/ Engineered by Starpath in California.
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Ryan JB Taylor retweeted
The software industry is apparently dying but job postings for software engineers are rapidly rising!
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Ryan JB Taylor retweeted
your timeline convinced you AI is in a bubble. talk to a boomer above the age 35 for 5 minutes. most people don’t even know what claude is.​​​​​​​​​​​​​​​​ kind of wild when you zoom out.
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Ryan JB Taylor retweeted
Careers are dead. Jobs are dying. Opportunities arising.
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Ryan JB Taylor retweeted
Project LUCI = AI Pin + Real-world memories + 🦞 We're turning AI from something you prompt into an assistant that actually knows you. Powered by Memories AI for visual memory and OpenClaw for real-world action. Join the waitlist.
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The universe gives us a lot to work with…we will never run out of thought-provoking content to share.
O Christmas tree, o cosmic tree 🎄 Located about 2,500 light-years from Earth, NGC 2264 is a cluster of young stars between one and five million years old. The stars appear as blue and white lights surrounded by swirls of gas—the “pine needles” of the tree—with green representing light in the visible spectrum. Learn more:go.nasa.gov/3MN1xdX
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People who say “we don’t want t-shirt factories in America” are mistaken about the very nature of technology. What they mean is that we don’t want Capex intense, heavily labor dependent, low margin manufacturing. But all of those attributes—capex, labor use, margin—are subject to technology, and therefore innovation. By exporting the industry, you are excluding the possibility of American technologists changing the *way t-shirts are made* in such a way that it is very profitable to do here. These sorts of changes are the inevitable direction of technology. Why would you count our civilization out of that?
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1/7 We needed power for building gas stations on the Moon and Mars. Suppliers quoted us $100/W for solar and 14 months lead times. So we built our own. Space-rated solar, scalable, and 90% cheaper. Now it powers our mission. And yours. Launching today 🧵
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