Jay Srinivasan

America’s leading health systems, like the Cleveland Clinic, work with @Luminai to eliminate administrative waste. We’re rapidly deploying to more health systems, and excited to announce Series B, bringing total funding to $60m.

Today, we're announcing Heaviside, our foundation model for electromagnetism. Trained on tens of millions of designs and over 20 years of proprietary simulation data, Heaviside predicts electromagnetic behavior from geometry in 13ms, which is 800,000x faster than a commercial solver. Heaviside is not a language model, and it’s not a surrogate model. Heaviside marks a new class of foundation model for physics which understands the fundamental relationships between materials, the geometries and the electromagnetic fields they generate. We’re releasing a research preview of Heaviside in Atlas RF Studio, an interactive agentic sandbox where you describe the EM behavior you want and the model generates the physical structure that produces it. @arenaphysica , we believe the implications of this class of model extend well beyond RF, as the frontier of exquisite hardware is electromagnetically-governed: wireless communication, radar, power delivery, high-speed computing, and the interconnects inside every chip on earth. In the months ahead, we’re excited to scale up Heaviside to broader frequency ranges, design spaces, and to support silicon-level designs, and deploy it with our closest partners and collaborators in service of their biggest design challenges. If you’ve read our thesis, this is just Step 2 in our pursuit of electromagnetic superintelligence. Read the full announcement and try Atlas RF Studio…tell us what you think: arenaphysica.com/publications/r…

Today, I’m excited to introduce @arenaphysica. For the past few years, we’ve been quietly partnering with companies pushing the frontier of hardware like @AMD , @anduriltech and @SiversSemicond  – deep in the guts of their most complex machines. Where applied physics, specifically the laws of electromagnetism, dictate performance. Electromagnetism is a domain poorly suited to LLMs, and a domain I spent most of my physics PhD trying to understand. At Arena Physica, we are in pursuit of electromagnetic superintelligence. We believe that a new class of foundation model will let humans push farther into our understanding of physics and will let us wield forces like EM that shape our world, but are fundamentally unintuitive to humans. It was an honor to partner with my favorite essayist, @packyM to explain how electromagnetism secretly runs the world

For IT, buy vs build math flipped. AI collapses the cost of building workflows. But maintaining integrations across every app, including the ones without APIs? That's still a nightmare. Build the workflow. Use infrastructure for the integrations.

Most AI projects start with the tool and skip the context: your rules, your edge cases, your processes. The ones that work start differently. "What does our process actually look like, in full detail?" Context first. Tool second.

The Hidden Advantage of Being Over 50 in the Age of AI ⁦@vayah_vikas⁩ flip.it/bsKRNr

Note @ankrgyl said run. Nobody wants to maintain five 9s on their own. That is a PITA. Managed services are still defensible

@esrtweet i largely agree with this article.. what it doesn't factor is distaste for maintenance & upkeep, or laziness.. after the initial sugar high of fast results wears off you've got to constantly maintain 'that thing' you built.. even with ai agents the ongoing dirty work is not fun

Feels like governments not actively working on this (even as a precaution) is a global phenomenon.

Wait for the diffusion …

@MohapatraHemant @SarvamAI @emergentlabs I think it's less you and more the insanely quick and aggressive optimization of X's current For You targeting algorithm. Go look for pictures of kittens and soon you'll realize everyone is only talking about kittens.

Is it just me or is everyone's entire x feed either @SarvamAI or @emergentlabs? Incredible stuff happening in Indian and India->global AI. Very bullish. 🙏❤️💪

@lalitinvestor I think this is spot on. A very simple second order implication is a lot of previously effective techniques become overloaded and ineffective. E.g., really worried about spam - if everyone can generate hyper personalized outbound email engines, do you stop relying on your inbox?

Everyone is discussing AI in the same way.. Jobs will go. New jobs will come. People who use AI will lead. Those who don’t will fall behind. Maybe all of that is true. But I think the bigger story is somewhere else. I have been thinking this for sometime.. Let us think one layer deeper.. First order impact is obvious: Productivity improves. Second order impact is more interesting: when effort drops, expectations rise. Earlier, if something took a week, people accepted it. With AI, the same work may take a day. Very soon, nobody will value speed because speed becomes default. The value shifts elsewhere… to judgement, originality, trust and decision quality. Third order impact: abundance creates noise. When everyone can generate content, code, designs or analysis quickly, the world doesn’t become smarter automatically. It becomes louder. The real scarcity may not be intelligence anymore, but credibility. People and brands that are trusted may become far more valuable than people who are simply efficient. Another angle most people miss: AI may reduce entry barriers, but increase survival pressure. Starting something will become easier. Finishing it, sustaining it and standing out may become harder. If every small company can operate like a large company, competition intensifies everywhere. There is also a human behaviour shift coming. When thinking becomes outsourced, will curiosity reduce? Or will humans actually think more because basic execution is automated? We might see two very different groups emerge. One that becomes passive users of AI, and another that uses AI to think deeper than ever before. Economically, AI could create strange outcomes.. Lower cost of work may increase supply so much that prices fall. Good for consumers. Tough for average producers. Margins may move from execution businesses towards distribution, trust networks and ownership of data. Even in fraud, regulation and compliance, the game changes. AI will help people hide complexity better. But it will also help others detect patterns faster. It becomes an arms race of intelligence rather than manpower. And maybe the biggest shift of all… For decades, knowledge itself was power. In an AI world, knowledge becomes cheap. The premium moves to wisdom, taste, ethics and the ability to ask the right questions. It could be a filter that separates humans who think from humans who simply execute. The real disruption may not be job loss. It may be a complete redefinition of what society values as “human contribution..

@businessbarista pmarchive.com/guide_to_start… From his article: When a great team meets a lousy market, market wins. When a lousy team meets a great market, market wins. When a great team meets a great market, something special happens.

If you f*ck up two specific decisions as an entrepreneur, you are toast. The first is who you choose to start a business with. The second is the market you choose to enter. If you get these decisions right, you can be a good entrepreneur & still build a MASSIVE business. If you get these decisions wrong, you can be a worldclass entrepreneur & very likely fail. I'm thankful to have crushed both of these decisions with @ArmanHezarkhani as my cofounder & the market that @tenex_labs lives in. We launched Tenex 11 months ago as McKinsey for AI & we've exceeded my expectations in short order. - Revenue of a Series B company - Bootstrapped & profitable since day 1 - Partnerships with major AI labs, Vercel, LangChain, Lovable - Highest talent density I've experienced (AI Engineers, AI Strategists, etc) - Winning Fortune 500 business against McKinsey & Company, Palantir Technologies, Accenture, etc. This is the highest growth, highest ceiling business I've ever built, and while I take some credit for the part I play in this, I owe the majority of our success to these decisions. Decision 1: picking the right co-founder 65% of failed startups fail because of co-founder conflict. This is a decision you just cannot afford to get wrong. And it's not an easy one to make. You need two things to line up perfectly: 1) Values alignment Mine: - Relentless pursuit of truth - Insatiable curiosity & playfulness - High integrity, high agency - A+ family person, A+ business person (in that order) 2) Complementary skillsets My skills: - Building trusted distribution - Creative thinking - EOS implementation My cofounder's skills: - 10x engineer - Savage seller & relationship builder - Exceptional operator Decision 2: picking the right market There is basically unlimited demand for Tenex right now. I've never seen anything like it. Hiring fast enough has been & continues to be our biggest bottleneck. This is entirely due to the market(s) that we chose to compete in. We sit at the intersection of two markets: one old & one new. The old market is 3rd party engineering services. It is a $3 trillion market & there is always-on demand for exceptional engineering talent as the productivity of good & great software engineers diverges in a post-AI world. The new market is AI transformation. Literally every (non-AI native) business on earth is trying to figure out how to go from AI-interested to AI-native & most companies are fumbling as they attempt transformation in-house. Tenex sits at the nexus of these markets, which is why our proverbial phone won't stop ringing. So, here's how I'd think about it. A business at its core is a collection of people making decisions & coordinating their actions based on those decisions. Some decisions are essential. Others are non-essential. But there are two decisions in particular, that if made properly, could make or break your success from the jump.

@MikeMacMike01 @draecomino It doesn't have to always be ship a public product that makes money. It's the internal stuff that you do day in and day out at work, and amplifying that. It's the difference between a more AI-aware marketer and a non-AI aware marketer. Or an HR person. Or an EA.

@draecomino What’s the gap? Aside from the skill set of configuring a bunch of agents, I’ve yet to see anything resembling a usable end product. All I see is engagement bait filling my X feed.

I've followed tech for 25 years and I've never felt a larger gap between the ~1 million people using Codex/Claude and the rest of humanity.

"Software is a stored process" - See the same pattern in IT. Every company's offboarding workflow is 90% standard and 10% institutional knowledge that lives in someone's head. That 10% is why automation fails. Moat moves from "can you build it" to "can you encode the process"

@NickAbraham12 If you get the economics and the cap table right.

YC realized agencies are fundamentally better businesses than ~75% of venture-backed startups.

Harder to be a VC than a founder right now. Not the work but the decision-making. Nobody knows what's defensible anymore. Founders can try to figure it out in real-time and pivot. VCs have to place 10 year bets in a landscape that changes every month.

"Model performance is going up, but the progress in implementing is not really because implementing this is hard stuff... And this what we call the deployment gap. ..And this deployment gap is what we can help to address" 10 points from @NandanNilekani today. Read in full. Excellent stuff. 1. Cannot Run Business The Old Way: "We cannot run business the old way & businesses have to change. The customer journeys, have to change. Talent will have to deal with a world where writing code will not be the goal. It will be actually making AI work, orchestration & those kind of things. So the jobs will change and operating model. How do we make this at scale? How do you get a firm with hundreds of employees to change all the things & make it work ?" 2. Massive 'Clean Up Job" -> "Modernization of legacy systems cannot be deferred anymore. What happened over the last 60-70, years, is people would not replace the legacy system. They just added to it. So if you go and look under the hood of a large enterprise, they will have mainframes from 1960 they will have mini computers from 1980 they will have land from 2000 they will have all kinds of things & all coexisting in silos. That is over. We if you really want a firm to take advantage of AI, you have to fundamentally clean this up. So this is a massive, massive clean up job.." 3. Tech Spends Won't Go Into Maintenance -> "60-80% of IT spend was on maintaining systems. There is no business value out of that. They want to go from 60% or 70% maintenance & 30% new systems to 30 -40% maintenance & 60-70% new business. They want to flip the way they spend money.." 4. Huge Requirement -> "..for the first time, because of AI, we have the tools now to do modernization fast & very quickly and in a much more economic way. So we have a huge demand, and we have the ability now to do it.. So fundamentally, accumulated tech debt over decades must be paid. You have no longer have the option to defer this. And this is a huge, huge requirement, and obviously it is a huge opportunity for us." 5. Build vs Buy Is A Big Opportunity For Us -> "..the balance of advantage is moving towards build rather than buy. And that is actually what is, if you see some of the concerns about what will happen to SaaS companies & all that - it's because of this - that building applications has become so simple that very often you may just build or you may replace something that which you which you bought, and with something to be built. This actually benefits folks like us. We will only build it for them. So fundamentally, it's good for us" 6. Using Agents - A Big Opportunity -> "Foundational systems, will increasingly become systems of record, but the interface, the interface will be agentic, because agentic interface makes a lot of sense.. Enterprises will want to put agentic layers on top of all their applications, even if they leave the system of record the same, and that is something which will be a combination of bought out agents, as well as building their own agents.. Again, that requires orchestration and work which somebody has to do. So there is a huge amount of work required once they go towards build rather than buy" 7. Technology Is Far Ahead Of It's Deployment -> "Technology is moving faster than the ability of enterprises to deploy it. .. the model performance is going up, but the progress in implementing is not really because implementing this is hard stuff. Fundamentally, it's about organizational change, business change, retraining your people, thinking about non deterministic approaches, changing your data so it's no longer in silos. So fundamentally, we have a situation where there is a deployment gap between the power of the technology and the capacity of businesses to use this. So if you guys think that some better product has come, nothing's going to happen, because the problem is here, not there. It's about how fast companies can implement you have to look at that. And this what we call the deployment gap. ..And this deployment gap is what we can help to address. So again, the very important point" 8. Retraining The Workforce -> "I think talent transformation is huge. We have all kinds of new roles, AI engineers, forward deployment engineers, AI leads, Forensic analysts, data. So fundamentally, the challenge will be, how do you take your workforce and make sure that they are reskilled and ready for the new new new business? And that's really the challenge that all the firms will face.. fundamentally, there will be a need for people, but they will be doing different things." 9. Greenfield vs Messy Real World -> "I can take a tool and give it to a kid & he will generate a million lines of code. But that is not the real world. The real world is the fact that companies have trillions of dollars invested in the systems. They have technical debt, they have data silos, they do not have documents. Somebody was telling me the other day that there are some old systems and on contract. They have guys as old as me, 70-75 year old guys, because nobody else knows what the hell is going on. And then, you know, when there is a crisis to be sorted out, they are pulled in from Phoenix or Florida or wherever they are, and they have to solve problems, and nobody else knows how to solve. So we have that kind of situation out there, undocumented dependencies. So taking brownfield systems and modernizing them is a hell of a lot more difficult than doing greenfield development. And lot of us get biased, because all the guys who talk about productivity are talking about greenfield development, and therefore getting these large enterprise organizations, productivity going is very, very different from individual tasks. It's a lot more complicated." 10. "There Is No opportunity Gap" -> "If anything, the opportunity is bigger than ever before. So do not get distracted by that. You should still ask the question, what is the firm doing to take advantage of this? What is the firm doing to transform its talent for this new world ? I'm sure everybody will not execute the same way. So there is an execution risk in doing that. So it is not an opportunity risk, it's an execution risk." @CNBCTV18News #stockmarkets #Nifty #ArtificialIntelligence #Infosys @Infosys #TCS #Wipro #HCLTech #TechMahindra #Investing #Anthropic #ChatGPT #OpenAI #AISummit