Ashot Arzumanyan

We plugged our vision agents (Isaac 0.2) into OpenClaw, turning vision into action.

Neurotech is getting hot. Our portfolio company @NeurosoftBio recently announced partnership with @ScienceCorp_: science.xyz/news/neurosoft…

The best conversations at @nvidia GTC won't happen in a session hall. Invite-only cocktail night for Neocloud execs, NCP leaders, sovereign AI operators, AI factory teams. No stage. No slides. Just infrastructure leaders comparing notes. Wed March 18 | 7-10pm | Downtown San Jose RSVP: luma.com/gtcsj-cocktail… #NVIDIAGTC

Multimodal data curation is harder than it looks. We're publishing our approach: meta-learning with independently optimized capabilities. Highly recommend following @NaveenSahi, who will be sharing more of our data strategies in the coming months.

VP Vance on his historic meeting with Armenian Prime Minister Pashinyan: “Peace is not made by people who are too focused on the past. Peace is made by people who are focused on the future...We're creating real prosperity for Armenia and the United States together.” 🇺🇸🤝🇦🇲

We are excited to share that Isaac-0.2 and 0.1 is now officially supported with vLLM! You can now simply launch a high performance inference engine with Isaac with a single line Thanks to the contributors! @oscardev256 github.com/KKSK-DON github.com/DarkLight1337 @rogerw0108

A park in #Yerevan today.

@ArmenAgha This is a very deep and interesting piece… I’d really love to see another piece of yours that analyses the current trend against parallels with Genesis 11:1-9 and the book of The Revelation of John.

I wrote something attempting this. The core claim is that AI can lift the curse of toil, but liberation requires solving two problems: how abundance reaches everyone, and how souls survive freedom. I draw on Chrysostom's distinction between work and toil, the desert fathers on what happens when necessity lifts, and the economics of Jubilee; pulling from Jewish, Orthodox, and Catholic tradition.

I'm surprised by how little deliberate effort there's been toward a spiritual interpretation of AI. We're creating something that resembles us in ways we don't fully understand. Most people have no framework for thinking about what these things are, and soon, no one will go a day without interacting with them.

@ArmenAgha No way - we missed each other in SF!

Killing an hour before my flight in a Russian Orthodox church. Two Russians come in, complaining it’s “собачий холод” (freezing) outside. It was 55°F. Peak assimilation.

Giving models native tools for perception is fun to watch. Lot's more work to do for models purpose built for the physical world. We'll probably OS this model soon!

Our models can now reason and use tools to zoom into images, allocating additional test-time compute when needed. Excited for you all to have your hands on this soon.

Is there an AI bubble? With the massive number of dollars going into AI infrastructure such as OpenAI’s $1.4 trillion plan and Nvidia briefly reaching a $5 trillion market cap, many have asked if speculation and hype have driven the values of AI investments above sustainable values. However, AI isn’t monolithic, and different areas look bubbly to different degrees. - AI application layer: There is underinvestment. The potential is still much greater than most realize. - AI infrastructure for inference: This still needs significant investment. - AI infrastructure for model training: I’m still cautiously optimistic about this sector, but there could also be a bubble. Caveat: I am absolutely not giving investment advice! AI application layer. There are many applications yet to be built over the coming decade using new AI technology. Almost by definition, applications that are built on top of AI infrastructure/technology (such as LLM APIs) have to be more valuable than the infrastructure, since we need them to be able to pay the infrastructure and technology providers. I am seeing many green shoots across many businesses that are applying agentic workflows, and am confident this will grow. I have also spoken with many Venture Capital investors who hesitate to invest in AI applications because they feel they don’t know how to pick winners, whereas the recipe for deploying $1B to build AI infrastructure is better understood. Some have also bought into the hype that almost all AI applications will be wiped out merely by frontier LLM companies improving their foundation models. Overall, I believe there is significant underinvestment in AI applications. This area remains a huge focus for my venture studio, AI Fund. AI infrastructure for inference. Despite AI’s low penetration today, infrastructure providers are already struggling to fulfill demand for processing power to generate tokens. Several of my teams are worried about whether we can get enough inference capacity, and both cost and inference throughput are limiting our ability to use even more. It is a good problem to have that businesses are supply-constrained rather than demand-constrained. The latter is a much more common problem, when not enough people want your product. But insufficient supply is nonetheless a problem, which is why I am glad our industry is investing significantly in scaling up inference capacity. As one concrete example of high demand for token generation, highly agentic coders are progressing rapidly. I’ve long been a fan of Claude Code; OpenAI Codex also improved dramatically with the release of GPT-5; and Gemini 3 has made Google CLI very competitive. As these tools improve, their adoption will grow. At the same time, overall market penetration is still low, and many developers are still using older generations of coding tools (and some aren’t even using any agentic coding tools). As market penetration grows — I’m confident it will, given how useful these tools are — aggregate demand for token generation will grow. I predicted early last year that we’d need more inference capacity, partly because of agentic workflows. Since then, the need has become more acute. As a society, we need more capacity for AI inference. Having said that, I’m not saying it’s impossible to lose money investing in this sector. If we end up overbuilding — and I don’t currently know if we will — then providers may end up having to sell capacity at a loss or at low returns. I hope investors in this space do well financially. The good news, however, is that even if we overbuild, this capacity will get used, and it will be good for application builders! AI infrastructure for model training. I am happy to see the investments going into training bigger models. But, of the three buckets of investments, this seems the riskiest. If open-source/open-weight models continue to grow in market share, then some companies that are pouring billions into training models might not see an attractive financial return on their investment. Additionally, algorithmic and hardware improvements are making it cheaper each year to train models of a given level of capability, so the “technology moat” for training frontier models is weak. (That said, ChatGPT has become a strong consumer brand, and so it enjoys a strong brand moat, while Gemini, assisted by Google's massive distribution advantage, is also making a strong showing.) I remain bullish about AI investments broadly. But what is the downside scenario — that is, is there a bubble that will pop? One scenario that worries me: If part of the AI stack (perhaps in training infra) suffers from overinvestment and collapses, it could lead to negative market sentiment around AI more broadly and an irrational outflow of interest away from investing in AI, despite the field overall having strong fundamentals. I don’t think this will happen, but if it does, it would be unfortunate since there’s still a lot of work in AI that I consider highly deserving of much more investment. Warren Buffett popularized Benjamin Graham’s quote, “In the short run, the market is a voting machine, but in the long run, it is a weighing machine.” He meant that in the short term, stock prices are driven by investor sentiment and speculation; but in the long term, they are driven by fundamental, intrinsic value. I find it hard to forecast sentiment and speculation, but am very confident about the long-term health of AI’s fundamentals. So my plan is just to keep building! [Original text: deeplearning.ai/the-batch/issu… ]

@NarenBriar Congrats Naren!! 🍾🎊🎉🎈

Ladies and gentlemen, it is an incredible honor to share that I have won my race for Bellevue City Council with 53% of the vote. For the first time in our nation's history, a Kurdish American has been elected to public office, and Bellevue made it happen. Many questioned whether this campaign's success was within the bounds of possibility. The odds, by any measure, were formidable. Yet I have observed that fortune seldom abandons those who possess the resolution to act upon their convictions. Our victory was a narrow, but healthy margin rather than an overwhelming mandate. This does not serve as discouragement, but rather as incitement, as is usual, to attempt something more full and satisfactory. I am now bound by a sacred obligation: to earn the confidence of those who withheld their support, and to justify the faith reposed in me by those who granted it. I am in deep gratitude for the honor you have entrusted to me, and will dedicate the remainder of my days in your service.

Coherence Neuro @coherenceneuro Raises $10 Million to Launch Closed-Loop Neuro System for Cancer Treatment. Coherence Neuro, a medical technology company working at the intersection of neurotechnology, neurobiology, and machine learning to improve the quality and length of life for people with cancer, led by Ben Woodington @WoodingtonBen, Elise Jenkins @Elise__Jenkins, José Lepe, and Jason Miranda, has secured a $10 million seed round led by Topology Ventures and Artesian @artesianvc(Alternative Investments), with participation from Blackbird @blackbirdvc, Possible Ventures, and a network of early-stage backers, including XEIA Venture Partners @XEIA_VP, Jumpspace Ventures, Divergent Capital, SmartGateVC @SmartGateVC, Spacewalk VC, and several prominent angels, including Matt Krisiloff @mattkrisiloff, Linhao Zhang, and Tim Shi.

#StartupInSpotlight: 🩺@coherenceneuro has secured a $10 million seed round. techfundingnews.com/startup-in-spo… The investment came from Topology Ventures, @artesianvc, @blackbird, Possible Ventures, XEIA Venture Partners, Jumpspace Ventures, @divergecap, @SmartGateVC, Spacewalk VC and others. @WoodingtonBen #news #startups #ustech #cancertherapy #healthtech #cancertreatment #neurosystem #funding #femalefounders #womenintech #investment #innovation

We've been selected for @AnthropicAI's Strategic Startup Program🚀 Beyond using @claudeai we'll work with their team, co-building our roadmap with one of the world’s leading AI research labs. Excited to build the next gen of #VoiceAI tech alongside Anthropic and continue pushing what’s possible in the AI era 🤝

Seeing a lot of grifting with start-ups pitching VCs that they are building world models for robotics when it's really lifting chinese video gen models. Have seen this happen twice in the last week.

@sama ChatGPT pro deep research sucks throughout last week or so… impossible to do any meaningful work! @ChatGPTapp needs eval in action… @OpenAI

very important work on a new eval

I’m excited to share one of the many open-weight releases soon to come. Introducing Isaac v0.1, our first release towards solving the first milestone in Physical AI; Perception.

@perceptroninc This is fantastic!!!

1/ Introducing Isaac 0.1 — our first perceptive-language model. 2B params, open weights. Matches or beats models significantly larger on core perception. We are pushing the efficient frontier for physical AI. perceptron.inc/blog/introduci…