Gurjeet Singh

YellowDog.ai just set a 10x benchmark uplift in scale computing, delivering 40,000 tasks per second (TPS) and managing 100,000 compute nodes in the cloud. What's even more interesting, that's 2x IBM Symphony and opens an intriguing pathway for these until-know closed/captive systems.

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Some of the most promising AI companies on my radar are targeting what my partners Jaya Gupta and Ashu Garg call “glue functions.” Historically, roles like RevOps, DevOps, SecOps, etc grew in popularity because someone needed to carry context across functions. Someone had to pull data from one system, check something in another, apply some judgment, and move the process forward. Traditional software captured state inside individual systems, but it rarely encoded the cross-functional logic that actually drives decisions. So companies created glue roles to bridge the gaps. In contrast, agents are perfectly suited for these roles - making glue functions a natural wedge for the next generation of AI companies. Check out their full p.o.v. here: https://lnkd.in/gV_u3ngU

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Ruokai Yin, our summer intern from Yale to Microsoft Azure, worked with Xuan Zuo, @Sattwik Deb Mishra, Hokchhay T. and Preyas Shah and me. We published a paper based on his project: https://lnkd.in/eHuG3BSt Summary of paper: Finding optimal sharding is hard especially for large LLMs. A large number of devices have to be divided into TP, PP, EP dimensions. For TP, we have to decide the tensor dimension e.g. head, hidden size to shard for every op. Additionally, TP can be one-dimensional/two-dimensional etc. This makes for a large sharding space that is hard to cover exhaustively. SoTA methods use heuristics to select sharding e.g. GSPMD, Sequence Parallelism, Helix etc. There are significant time gaps, months or years, between the discovery of each of these methods. We want to fast-forward the process of discovery by 1) using a formal representation of the sharding space, and, 2) using reinforcement learning to search the sharding space.    This paper demonstrates that our method is 3.5x better than random search, and on par with hand-tuned heuristics when looking only at 1D sharding and without allowing sequence parallelism. We aim to extend the work to multi-dimensional sharding as well as sequence parallelism. Beyond that, we wish to combine the problem of sharding with fusion, tiling and collectives as well.

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Chris Lattner's take on CCC (Claude C Compiler) is a great summary of where AI's capabilities are at, in terms of generating code: https://lnkd.in/gGYapXVa Would suggest reading this along with Drew Breunig's take on Claude being an electron app: https://lnkd.in/g7xUsbss

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I was listening to this https://lnkd.in/giycVEkG and had a thought I can't shake: What if foundation models are AI's genetic code? DNA gives us instincts... babies cry, calves walk to milk without learning. Foundation models feel similar: pre-loaded with language and reasoning. But evolution doesn't create one perfect organism. It creates fish, mammals, birds... each specialized. Maybe AI works the same way? Not one AGI, but multiple species and individuals that learn and evolve. If foundation models are our "genetic code"... we need to be careful what we embed there. These become instincts for all future AI. Like Sutton says, learning is fundamental and the next crucial piece: how do we take what AI systems learn and feed it back to the next generation? In biology, successful traits get passed down... what's the AI equivalent? Maybe we're not just building better AI... maybe we're designing artificial evolution.

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The recent Waymo testimony making the rounds isn’t really about robotaxis. It’s about the reality gap between AI demos and AI at scale in the real world. In Senate testimony earlier this month, Waymo confirmed that when its autonomous vehicles encounter ambiguous situations, they can request guidance from remote human agents. The vehicles remain in control, but humans are still in the loop. This shouldn’t be read as “AI failure.” It should be read as a reminder of how hard real-world deployment actually is. Every serious AI system that touches safety, money, or operations ends up with: • human fallback layers • escalation paths • edge-case handling • governance and oversight That’s not a bug. That’s operational maturity. The lesson for leaders isn’t “autonomy is fake.” It’s this: scaling AI safely is less about models, and more about systems. Real-world AI = model + data + workflow + humans + policy + monitoring. If you ignore any of those layers, you don’t get innovation. You get incidents. We’re entering the phase of AI where credibility won’t come from benchmarks or demos, but from disciplined deployment: where safety, transparency, and operational design matter as much as algorithms. The companies that understand that will scale. The ones that don’t will stall — or worse. #AILeadership #HumanCenteredAI #EnterpriseAI #AITransformation #AIatScale https://lnkd.in/eVVfiUPP

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